Journal articles: 'Inductive supervised learning'

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Wu, Haiping, Khimya Khetarpal, and Doina Precup. "Self-Supervised Attention-Aware Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10311–19. http://dx.doi.org/10.1609/aaai.v35i12.17235.

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Visual saliency has emerged as a major visualization tool for interpreting deep reinforcement learning (RL) agents. However, much of the existing research uses it as an analyzing tool rather than an inductive bias for policy learning. In this work, we use visual attention as an inductive bias for RL agents. We propose a novel self-supervised attention learning approach which can 1. learn to select regions of interest without explicit annotations, and 2. act as a plug for existing deep RL methods to improve the learning performance. We empirically show that the self-supervised attention-aware deep RL methods outperform the baselines in the context of both the rate of convergence and performance. Furthermore, the proposed self-supervised attention is not tied with specific policies, nor restricted to a specific scene. We posit that the proposed approach is a general self-supervised attention module for multi-task learning and transfer learning, and empirically validate the generalization ability of the proposed method. Finally, we show that our method learns meaningful object keypoints highlighting improvements both qualitatively and quantitatively.

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Bisio, Federica, Sergio Decherchi, Paolo Gastaldo, and Rodolfo Zunino. "Inductive bias for semi-supervised extreme learning machine." Neurocomputing 174 (January 2016): 154–67. http://dx.doi.org/10.1016/j.neucom.2015.04.104.

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Hovsepian, Karen, Peter Anselmo, and Subhasish Mazumdar. "Supervised inductive learning with Lotka–Volterra derived models." Knowledge and Information Systems 26, no. 2 (January 16, 2010): 195–223. http://dx.doi.org/10.1007/s10115-009-0280-5.

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Juan, Liu, and Li Weihua. "A hybrid genetic algorithm for supervised inductive learning." Wuhan University Journal of Natural Sciences 1, no. 3-4 (December 1996): 611–16. http://dx.doi.org/10.1007/bf02900895.

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B, Amarnath, and S. Appavu alias Balamurugan. "Feature Selection for Supervised Learning via Dependency Analysis." Journal of Computational and Theoretical Nanoscience 13, no. 10 (October 1, 2016): 6885–91. http://dx.doi.org/10.1166/jctn.2016.5642.

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A new feature selection method based on Inductive probability is proposed in this paper. The main idea is to find the dependent attributes and remove the redundant ones among them. The technology to obtain the dependency needed is based on Inductive probability approach. The purpose of the proposed method is to reduce the computational complexity and increase the classification accuracy of the selected feature subsets. The dependence between two attributes is determined based on the probabilities of their joint values that contribute to positive and negative classification decisions. If there is an opposing set of attribute values that do not lead to opposing classification decisions (zero probability), the two attributes are considered independent, otherwise dependent. One of them can be removed and thus the number of attributes is reduced. A new attribute selection algorithm with Inductive probability is implemented and evaluated through extensive experiments, comparing with related attribute selection algorithms over eight datasets such as Molecular Biology, Connect4, Soybean, Zoo, Ballon, Mushroom, Lenses and Fictional from UCI Machine Learning Repository databases.

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Zhu, Ruifeng, Fadi Dornaika, and Yassine Ruichek. "Inductive semi-supervised learning with Graph Convolution based regression." Neurocomputing 434 (April 2021): 315–22. http://dx.doi.org/10.1016/j.neucom.2020.12.084.

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Yang, Shuyi, Dino Ienco, Roberto Esposito, and Ruggero G. Pensa. "ESA☆: A generic framework for semi-supervised inductive learning." Neurocomputing 447 (August 2021): 102–17. http://dx.doi.org/10.1016/j.neucom.2021.03.051.

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Dornaika, F., R. Dahbi, A. Bosaghzadeh, and Y. Ruichek. "Efficient dynamic graph construction for inductive semi-supervised learning." Neural Networks 94 (October 2017): 192–203. http://dx.doi.org/10.1016/j.neunet.2017.07.006.

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Zhang, Zhao, Lei Jia, Mingbo Zhao, Qiaolin Ye, Min Zhang, and Meng Wang. "Adaptive non-negative projective semi-supervised learning for inductive classification." Neural Networks 108 (December 2018): 128–45. http://dx.doi.org/10.1016/j.neunet.2018.07.017.

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Tian, Xilan, Gilles Gasso, and Stéphane Canu. "A multiple kernel framework for inductive semi-supervised SVM learning." Neurocomputing 90 (August 2012): 46–58. http://dx.doi.org/10.1016/j.neucom.2011.12.036.

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Ye, Han-Jia, Xin-Chun Li, and De-Chuan Zhan. "Task Cooperation for Semi-Supervised Few-Shot Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 10682–90. http://dx.doi.org/10.1609/aaai.v35i12.17277.

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Training a model with limited data is an essential task for machine learning and visual recognition. Few-shot learning approaches meta-learn a task-level inductive bias from SEEN class few-shot tasks, and the meta-model is expected to facilitate the few-shot learning with UNSEEN classes. Inspired by the idea that unlabeled data can be utilized to smooth the model space in traditional semi-supervised learning, we propose TAsk COoperation (TACO) which takes advantage of unsupervised tasks to smooth the meta-model space. Specifically, we couple the labeled support set in a few-shot task with easily-collected unlabeled instances, prediction agreement on which encodes the relationship between tasks. The learned smooth meta-model promotes the generalization ability on supervised UNSEEN few-shot tasks. The state-of-the-art few-shot classification results on MiniImageNet and TieredImageNet verify the superiority of TACO to leverage unlabeled data and task relationship in meta-learning.

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Krstacic, A., G. Krstacic, D. Gamberger, and Z. Car. "T04-P-028 Stroke patient models based on supervised inductive machine learning." Atherosclerosis Supplements 6, no. 1 (April 2005): 158–59. http://dx.doi.org/10.1016/s1567-5688(05)80619-1.

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Lokesh Kumar, T. N., and Bhaskarjyoti Das. "An evaluation of approaches for enhancing inductive learning with a transductive view." Journal of Physics: Conference Series 2161, no. 1 (January 1, 2022): 012048. http://dx.doi.org/10.1088/1742-6596/2161/1/012048.

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Abstract Availability of enough labeled data is a challenge for most inductive learners who try to generalize based on limited labeled dataset. A traditional semi-supervised approach for the same problem attempts to approach it by methods such as wrapping multiple inductive learners on derived pseudo-labels, unsupervised feature extraction or suitable modification of the objective function. In this work, a simple approach is adopted whereby an inductive learner is enhanced by suitably enabling it with a transductive view of the data. The experiments, though conducted on a small dataset, successfully provide few insights i.e. transductive view benefits an inductive learner, a transductive view that considers both attribute and relations is more effective than one that considers either attributes or relations and graph convolution based embedding algorithms effectively captures the information from transductive views compared to popular knowledge embedding approaches.

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Marino, Dante, and Guglielmo Tamburrini. "Learning robots and human responsibility." International Review of Information Ethics 6 (December 1, 2006): 46–51. http://dx.doi.org/10.29173/irie139.

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Epistemic limitations concerning prediction and explanation of the behaviour of robots that learn from experience are selectively examined by reference to machine learning methods and computational theories of supervised inductive learning. Moral responsibility and liability ascription problems concerning damages caused by learning robot actions are discussed in the light of these epistemic limitations. In shaping responsibility ascription policies one has to take into account the fact that robots and softbots – by combining learning with autonomy, pro-activity, reasoning, and planning – can enter cognitive interactions that human beings have not experienced with any other non-human system.

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Yang, Tianchi, Linmei Hu, Chuan Shi, Houye Ji, Xiaoli Li, and Liqiang Nie. "HGAT: Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification." ACM Transactions on Information Systems 39, no. 3 (May 6, 2021): 1–29. http://dx.doi.org/10.1145/3450352.

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Short text classification has been widely explored in news tagging to provide more efficient search strategies and more effective search results for information retrieval. However, most existing studies, concentrating on long text classification, deliver unsatisfactory performance on short texts due to the sparsity issue and the insufficiency of labeled data. In this article, we propose a novel heterogeneous graph neural network-based method for semi-supervised short text classification, leveraging full advantage of limited labeled data and large unlabeled data through information propagation along the graph. Specifically, we first present a flexible heterogeneous information network (HIN) framework for modeling short texts, which can integrate any type of additional information and meanwhile capture their relations to address the semantic sparsity. Then, we propose Heterogeneous Graph Attention networks (HGAT) to embed the HIN for short text classification based on a dual-level attention mechanism, including node-level and type-level attentions. To efficiently classify new coming texts that do not previously exist in the HIN, we extend our model HGAT for inductive learning, avoiding re-training the model on the evolving HIN. Extensive experiments on single-/multi-label classification demonstrates that our proposed model HGAT significantly outperforms state-of-the-art methods across the benchmark datasets under both transductive and inductive learning.

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Wang, Yuhong, and Xin Li. "Neural-Guided Inductive Synthesis of Functional Programs on List Manipulation by Offline Supervised Learning." IEEE Access 9 (2021): 71521–34. http://dx.doi.org/10.1109/access.2021.3079351.

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Todorovski, Ljupco, Will Bridewell, and Pat Langley. "Discovering Constraints for Inductive Process Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 256–62. http://dx.doi.org/10.1609/aaai.v26i1.8152.

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Scientists use two forms of knowledge in the construction ofexplanatory models: generalized entities and processes that relatethem; and constraints that specify acceptable combinations of thesecomponents. Previous research on inductive process modeling, whichconstructs models from knowledge and time-series data, has relied onhandcrafted constraints. In this paper, we report an approach todiscovering such constraints from a set of models that have beenranked according to their error on observations. Our approach adaptsinductive techniques for supervised learning to identify processcombinations that characterize accurate models. We evaluate themethod's ability to reconstruct known constraints and to generalizewell to other modeling tasks in the same domain. Experiments with synthetic data indicate that the approach can successfully reconstructknown modeling constraints. Another study using natural data suggests that transferring constraints acquired from one modeling scenario to another within the same domain considerably reduces the amount of search for candidate model structures while retaining the most accurate ones.

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Kobylarz, Jhonatan, Jordan J. Bird, Diego R. Faria, Eduardo Parente Ribeiro, and Anikó Ekárt. "Thumbs up, thumbs down: non-verbal human-robot interaction through real-time EMG classification via inductive and supervised transductive transfer learning." Journal of Ambient Intelligence and Humanized Computing 11, no. 12 (March 7, 2020): 6021–31. http://dx.doi.org/10.1007/s12652-020-01852-z.

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AbstractIn this study, we present a transfer learning method for gesture classification via an inductive and supervised transductive approach with an electromyographic dataset gathered via the Myo armband. A ternary gesture classification problem is presented by states of ’thumbs up’, ’thumbs down’, and ’relax’ in order to communicate in the affirmative or negative in a non-verbal fashion to a machine. Of the nine statistical learning paradigms benchmarked over 10-fold cross validation (with three methods of feature selection), an ensemble of Random Forest and Support Vector Machine through voting achieves the best score of 91.74% with a rule-based feature selection method. When new subjects are considered, this machine learning approach fails to generalise new data, and thus the processes of Inductive and Supervised Transductive Transfer Learning are introduced with a short calibration exercise (15 s). Failure of generalisation shows that 5 s of data per-class is the strongest for classification (versus one through seven seconds) with only an accuracy of 55%, but when a short 5 s per class calibration task is introduced via the suggested transfer method, a Random Forest can then classify unseen data from the calibrated subject at an accuracy of around 97%, outperforming the 83% accuracy boasted by the proprietary Myo system. Finally, a preliminary application is presented through social interaction with a humanoid Pepper robot, where the use of our approach and a most-common-class metaclassifier achieves 100% accuracy for all trials of a ‘20 Questions’ game.

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Cao, Yun-Hao, and Jianxin Wu. "A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 194–202. http://dx.doi.org/10.1609/aaai.v36i1.19894.

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This paper starts by revealing a surprising finding: without any learning, a randomly initialized CNN can localize objects surprisingly well. That is, a CNN has an inductive bias to naturally focus on objects, named as Tobias ("The object is at sight") in this paper. This empirical inductive bias is further analyzed and successfully applied to self-supervised learning (SSL). A CNN is encouraged to learn representations that focus on the foreground object, by transforming every image into various versions with different backgrounds, where the foreground and background separation is guided by Tobias. Experimental results show that the proposed Tobias significantly improves downstream tasks, especially for object detection. This paper also shows that Tobias has consistent improvements on training sets of different sizes, and is more resilient to changes in image augmentations.

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Geraldeli Rossi, Rafael, Alneu de Andrade Lopes, and Solange Oliveira Rezende. "Using bipartite heterogeneous networks to speed up inductive semi-supervised learning and improve automatic text categorization." Knowledge-Based Systems 132 (September 2017): 94–118. http://dx.doi.org/10.1016/j.knosys.2017.06.016.

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Dubba, Krishna S. R., Anthony G. Cohn, David C. Hogg, Mehul Bhatt, and Frank Dylla. "Learning Relational Event Models from Video." Journal of Artificial Intelligence Research 53 (May 27, 2015): 41–90. http://dx.doi.org/10.1613/jair.4395.

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Event models obtained automatically from video can be used in applications ranging from abnormal event detection to content based video retrieval. When multiple agents are involved in the events, characterizing events naturally suggests encoding interactions as relations. Learning event models from this kind of relational spatio-temporal data using relational learning techniques such as Inductive Logic Programming (ILP) hold promise, but have not been successfully applied to very large datasets which result from video data. In this paper, we present a novel framework REMIND (Relational Event Model INDuction) for supervised relational learning of event models from large video datasets using ILP. Efficiency is achieved through the learning from interpretations setting and using a typing system that exploits the type hierarchy of objects in a domain. The use of types also helps prevent over generalization. Furthermore, we also present a type-refining operator and prove that it is optimal. The learned models can be used for recognizing events from previously unseen videos. We also present an extension to the framework by integrating an abduction step that improves the learning performance when there is noise in the input data. The experimental results on several hours of video data from two challenging real world domains (an airport domain and a physical action verbs domain) suggest that the techniques are suitable to real world scenarios.

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Evans, Richard, and Edward Grefenstette. "Learning Explanatory Rules from Noisy Data." Journal of Artificial Intelligence Research 61 (January 26, 2018): 1–64. http://dx.doi.org/10.1613/jair.5714.

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Artificial Neural Networks are powerful function approximators capable of modelling solutions to a wide variety of problems, both supervised and unsupervised. As their size and expressivity increases, so too does the variance of the model, yielding a nearly ubiquitous overfitting problem. Although mitigated by a variety of model regularisation methods, the common cure is to seek large amounts of training data--which is not necessarily easily obtained--that sufficiently approximates the data distribution of the domain we wish to test on. In contrast, logic programming methods such as Inductive Logic Programming offer an extremely data-efficient process by which models can be trained to reason on symbolic domains. However, these methods are unable to deal with the variety of domains neural networks can be applied to: they are not robust to noise in or mislabelling of inputs, and perhaps more importantly, cannot be applied to non-symbolic domains where the data is ambiguous, such as operating on raw pixels. In this paper, we propose a Differentiable Inductive Logic framework, which can not only solve tasks which traditional ILP systems are suited for, but shows a robustness to noise and error in the training data which ILP cannot cope with. Furthermore, as it is trained by backpropagation against a likelihood objective, it can be hybridised by connecting it with neural networks over ambiguous data in order to be applied to domains which ILP cannot address, while providing data efficiency and generalisation beyond what neural networks on their own can achieve.

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Wang, Chenwei, Xiaoyu Liu, Yulin Huang, Siyi Luo, Jifang Pei, Jianyu Yang, and Deqing Mao. "Semi-Supervised SAR ATR Framework with Transductive Auxiliary Segmentation." Remote Sensing 14, no. 18 (September 12, 2022): 4547. http://dx.doi.org/10.3390/rs14184547.

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Convolutional neural networks (CNNs) have achieved high performance in synthetic aperture radar (SAR) automatic target recognition (ATR). However, the performance of CNNs depends heavily on a large amount of training data. The insufficiency of labeled training SAR images limits the recognition performance and even invalidates some ATR methods. Furthermore, under few labeled training data, many existing CNNs are even ineffective. To address these challenges, we propose a Semi-supervised SAR ATR Framework with transductive Auxiliary Segmentation (SFAS). The proposed framework focuses on exploiting the transductive generalization on available unlabeled samples with an auxiliary loss serving as a regularizer. Through auxiliary segmentation of unlabeled SAR samples and information residue loss (IRL) in training, the framework can employ the proposed training loop process and gradually exploit the information compilation of recognition and segmentation to construct a helpful inductive bias and achieve high performance. Experiments conducted on the MSTAR dataset have shown the effectiveness of our proposed SFAS for few-shot learning. The recognition performance of 94.18% can be achieved under 20 training samples in each class with simultaneous accurate segmentation results. Facing variances of EOCs, the recognition ratios are higher than 88.00% when 10 training samples each class.

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Foulds, James, and Eibe Frank. "A review of multi-instance learning assumptions." Knowledge Engineering Review 25, no. 1 (March 2010): 1–25. http://dx.doi.org/10.1017/s026988890999035x.

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AbstractMulti-instance (MI) learning is a variant of inductive machine learning, where each learning example contains a bag of instances instead of a single feature vector. The term commonly refers to the supervised setting, where each bag is associated with a label. This type of representation is a natural fit for a number of real-world learning scenarios, including drug activity prediction and image classification, hence many MI learning algorithms have been proposed. Any MI learning method must relate instances to bag-level class labels, but many types of relationships between instances and class labels are possible. Although all early work in MI learning assumes a specific MI concept class known to be appropriate for a drug activity prediction domain; this ‘standard MI assumption’ is not guaranteed to hold in other domains. Much of the recent work in MI learning has concentrated on a relaxed view of the MI problem, where the standard MI assumption is dropped, and alternative assumptions are considered instead. However, often it is not clearly stated what particular assumption is used and how it relates to other assumptions that have been proposed. In this paper, we aim to clarify the use of alternative MI assumptions by reviewing the work done in this area.

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Fop, Michael, Pierre-Alexandre Mattei, Charles Bouveyron, and Thomas Brendan Murphy. "Unobserved classes and extra variables in high-dimensional discriminant analysis." Advances in Data Analysis and Classification 16, no. 1 (March 2022): 55–92. http://dx.doi.org/10.1007/s11634-021-00474-3.

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AbstractIn supervised classification problems, the test set may contain data points belonging to classes not observed in the learning phase. Moreover, the same units in the test data may be measured on a set of additional variables recorded at a subsequent stage with respect to when the learning sample was collected. In this situation, the classifier built in the learning phase needs to adapt to handle potential unknown classes and the extra dimensions. We introduce a model-based discriminant approach, Dimension-Adaptive Mixture Discriminant Analysis (D-AMDA), which can detect unobserved classes and adapt to the increasing dimensionality. Model estimation is carried out via a full inductive approach based on an EM algorithm. The method is then embedded in a more general framework for adaptive variable selection and classification suitable for data of large dimensions. A simulation study and an artificial experiment related to classification of adulterated honey samples are used to validate the ability of the proposed framework to deal with complex situations.

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Albertengo, G., and W. Hassan. "SHORT TERM URBAN TRAFFIC FORECASTING USING DEEP LEARNING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W7 (September 20, 2018): 3–10. http://dx.doi.org/10.5194/isprs-annals-iv-4-w7-3-2018.

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<p><strong>Abstract.</strong> In today’s world, the number of vehicles is increasing rapidly in developing countries and China and remains stable in all other countries, while road infrastructure mostly remains unchanged, causing congestion problems in many cities. Urban Traffic Control systems can be helpful in counteracting congestion if they receive accurate information on traffic flow. So far, these data are collected by sensors on roads, such as Inductive Loops, which are rather expensive to install and maintain. A less expensive approach could be to use a limited number of sensors combined with Artificial Intelligence to forecast the intensity of traffic at any point in a city. In this paper, we propose a simple yet accurate short-term urban traffic forecasting solution applying supervised window-based regression analysis using Deep Learning algorithm. Experimental results show that is it possible to forecast the intensity of traffic with good accuracy just monitoring its intensity in the last few minutes. The most significant result, in our opinion, is that the machine can generate accurate predictions even with no knowledge of the current time, the day of the week or the type of the day (holiday, weekday, etc).</p>

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Chen, Zhiwei, Changan Wang, Yabiao Wang, Guannan Jiang, Yunhang Shen, Ying Tai, Chengjie Wang, Wei Zhang, and Liujuan Cao. "LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 410–18. http://dx.doi.org/10.1609/aaai.v36i1.19918.

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Weakly supervised object localization (WSOL) aims to learn object localizer solely by using image-level labels. The convolution neural network (CNN) based techniques often result in highlighting the most discriminative part of objects while ignoring the entire object extent. Recently, the transformer architecture has been deployed to WSOL to capture the long-range feature dependencies with self-attention mechanism and multilayer perceptron structure. Nevertheless, transformers lack the locality inductive bias inherent to CNNs and therefore may deteriorate local feature details in WSOL. In this paper, we propose a novel framework built upon the transformer, termed LCTR (Local Continuity TRansformer), which targets at enhancing the local perception capability of global features among long-range feature dependencies. To this end, we propose a relational patch-attention module (RPAM), which considers cross-patch information on a global basis. We further design a cue digging module (CDM), which utilizes local features to guide the learning trend of the model for highlighting the weak local responses. Finally, comprehensive experiments are carried out on two widely used datasets, ie, CUB-200-2011 and ILSVRC, to verify the effectiveness of our method.

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GORDON, HEATHER, NICOLINO J. PIZZI, JON M. GERRARD, and RAY SOMORJAI. "ASSESSMENT OF BLEEDING PREDISPOSITIONS IN TONSILLECTOMY/ADENOIDECTOMY PATIENTS USING NON-METRIC CLUSTERING." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 03 (June 1995): 557–64. http://dx.doi.org/10.1142/s0218001495000523.

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A questionnaire, designed to assess bleeding predispositions in tonsillectomy and/or adenoidectomy patients, was administered to 236 otherwise healthy children. For comparative purposes, 114 patients with bleeding disorders were also studied. An unsupervised non-metric clustering technique was used in an attempt to classify bleeders against non-bleeders based solely on the responses to the questionnaire. Non-metric techniques are essential for the classification process because of the large number of missing attribute values in the patient data set. As a benchmark, a supervised inductive machine learning strategy was also used to classify the patients. Performance results are compared and contrasted between the techniques across different subsets of the patient data. These techniques are also evaluated as a methodology for determining the relative significance of attributes vis-à-vis the reduction of the dimensionality of a large medical data set. In this investigation, the classification rate achieved using the non-metric technique (73%) was only marginally poorer than the rate using the supervised technique (76%). Moreover, these results were obtained with an accompanying 80% reduction in the number of attributes used to perform the analysis.

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Mo, Mingzhu. "Application of GPS and Accelerometers in Predicting Physical Activity Patterns." Mathematical Problems in Engineering 2022 (April 29, 2022): 1–5. http://dx.doi.org/10.1155/2022/8093703.

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To reduce the workload, to predict the physical activity mode with fewer variables, and to construct a path to predict PAM based on temporal and spatial data generated by physical activity and the amount of activity, this paper mainly uses the literature, logical analysis, and inductive method to sort out and summarize the basic methods and models in predicting physical activity mode using GPS and accelerometer at home and abroad and to construct a path from equipment. The process involves selecting and determining the predictors, collecting data, and using supervised learning algorithms and unsupervised learning algorithms. The joint use of GPS and accelerometers is fully capable of predicting physical activity patterns and can realize the method of predicting physical activity patterns based on the spatiotemporal data and the amount of activity generated by physical activity, although GPS and accelerometers have shortcomings in predicting PAM in terms of positioning error, missing data, and wearing position and mode.

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Cracknell, Matthew J., and Anya M. Reading. "The upside of uncertainty: Identification of lithology contact zones from airborne geophysics and satellite data using random forests and support vector machines." GEOPHYSICS 78, no. 3 (May 1, 2013): WB113—WB126. http://dx.doi.org/10.1190/geo2012-0411.1.

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Inductive machine learning algorithms attempt to recognize patterns in, and generalize from empirical data. They provide a practical means of predicting lithology, or other spatially varying physical features, from multidimensional geophysical data sets. It is for this reason machine learning approaches are increasing in popularity for geophysical data inference. A key motivation for their use is the ease with which uncertainty measures can be estimated for nonprobabilistic algorithms. We have compared and evaluated the abilities of two nonprobabilistic machine learning algorithms, random forests (RF) and support vector machines (SVM), to recognize ambiguous supervised classification predictions using uncertainty calculated from estimates of class membership probabilities. We formulated a method to establish optimal uncertainty threshold values to identify and isolate the maximum number of incorrect predictions while preserving most of the correct classifications. This is illustrated using a case example of the supervised classification of surface lithologies in a folded, structurally complex, metamorphic terrain. We found that (1) the use of optimal uncertainty thresholds significantly improves overall classification accuracy of RF predictions, but not those of SVM, by eliminating the maximum number of incorrectly classified samples while preserving the maximum number of correctly classified samples; (2) RF, unlike SVM, was able to exploit dependencies and structures contained within spatially varying input data; and (3) high RF prediction uncertainty is spatially coincident with transitions in lithology and associated contact zones, and regions of intense deformation. Uncertainty has its upside in the identification of areas of key geologic interest and has wide application across the geosciences, where transition zones are important classes in their own right. The techniques used in this study are of practical value in prioritizing subsequent geologic field activities, which, with the aid of this analysis, may be focused on key lithology contacts and problematic localities.

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Maphalala, Mncedisi Christian, Rachel Gugu Mkhasibe, and Dumisani Wilfred Mncube. "Online Learning as a Catalyst for Self-directed Learning in Universities during the COVID-19 Pandemic." Research in Social Sciences and Technology 6, no. 2 (September 29, 2021): 233–48. http://dx.doi.org/10.46303/ressat.2021.25.

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The COVID-19 pandemic accelerated the use of online learning and self-directed learning to motivate and engage students. Therefore, this study sought to determine how online learning fostered self-directed learning at a South African university during this period. Higher education institutions worldwide had to shut down indefinitely following guidance from health experts to contain the spread of the COVID-19 pandemic. Since education is regarded as a pillar of development for all countries, some means had to be found to keep teaching and learning going irrespective of the ongoing health crisis. Hence, online learning made it possible for university students to continue learning during the emergency university closure. This was a period of reckoning, however, as many students began experiencing challenges related to poor internet connectivity and accessing digital learning devices. Despite these challenges, the solution was to reach out to all students to ensure that they were not excluded from the learning process. The sudden transition to online learning meant that students could no longer follow a well-coordinated, structured learning schedule that was guided and supervised on campus; rather, online learning meant they had to become more independent in their learning. Independent learning encourages students to be proactive and independent, a philosophy aligned to self-directed learning (SDL). This study explored the experiences of third-year student teachers in navigating SDL through online learning platforms. The study was underpinned by self-directed learning theory and adopted a qualitative case study research design, generating data from ten student teachers using a Zoom App focus group discussion. Data were analyzed using an inductive thematic analysis framework. The study found that although SDL is appropriate because it promotes learning independently, the majority of student teachers encountered several challenges when adopting online learning, catching them off guard because they were not formally introduced to it.

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Miller, David J., Jayaram Raghuram, George Kesidis, and Christopher M. Collins. "Improved Generative Semisupervised Learning Based on Finely Grained Component-Conditional Class Labeling." Neural Computation 24, no. 7 (July 2012): 1926–66. http://dx.doi.org/10.1162/neco_a_00284.

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We introduce new inductive, generative semisupervised mixtures with more finely grained class label generation mechanisms than in previous work. Our models combine advantages of semisupervised mixtures, which achieve label extrapolation over a component, and nearest-neighbor (NN)/nearest-prototype (NP) classification, which achieve accurate classification in the vicinity of labeled samples or prototypes. For our NN-based method, we propose a novel two-stage stochastic data generation, with all samples first generated using a standard finite mixture and then all class labels generated, conditioned on the samples and their components of origin. This mechanism entails an underlying Markov random field, specific to each mixture component or cluster. We invoke the pseudo-likelihood formulation, which forms the basis for an approximate generalized expectation-maximization model learning algorithm. Our NP-based model overcomes a problem with the NN-based model that manifests at very low labeled fractions. Both models are advantageous when within-component class proportions are not constant over the feature space region “owned by” a component. The practicality of this scenario is borne out by experiments on UC Irvine data sets, which demonstrate significant gains in classification accuracy over previous semisupervised mixtures and also overall gains, over KNN classification. Moreover, for very small labeled fractions, our methods overall outperform supervised linear and nonlinear kernel support vector machines.

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Sharifzadeh, Sahand, Sina Moayed Baharlou, and Volker Tresp. "Classification by Attention: Scene Graph Classification with Prior Knowledge." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (May 18, 2021): 5025–33. http://dx.doi.org/10.1609/aaai.v35i6.16636.

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A major challenge in scene graph classification is that the appearance of objects and relations can be significantly different from one image to another. Previous works have addressed this by relational reasoning over all objects in an image or incorporating prior knowledge into classification. Unlike previous works, we do not consider separate models for perception and prior knowledge. Instead, we take a multi-task learning approach by introducing schema representations and implementing the classification as an attention layer between image-based representations and the schemata. This allows for the prior knowledge to emerge and propagate within the perception model. By enforcing the model also to represent the prior, we achieve a strong inductive bias. We show that our model can accurately generate commonsense knowledge and that the iterative injection of this knowledge to scene representations, as a top-down mechanism, leads to significantly higher classification performance. Additionally, our model can be fine-tuned on external knowledge given as triples. When combined with self-supervised learning and with 1% of annotated images only, this gives more than 3% improvement in object classification, 26% in scene graph classification, and 36% in predicate prediction accuracy.

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Feldman, Vitaly, and Leslie G. Valiant. "Experience-Induced Neural Circuits That Achieve High Capacity." Neural Computation 21, no. 10 (October 2009): 2715–54. http://dx.doi.org/10.1162/neco.2009.08-08-851.

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Over a lifetime, cortex performs a vast number of different cognitive actions, mostly dependent on experience. Previously it has not been known how such capabilities can be reconciled, even in principle, with the known resource constraints on cortex, such as low connectivity and low average synaptic strength. Here we describe neural circuits and associated algorithms that respect the brain's most basic resource constraints and support the execution of high numbers of cognitive actions when presented with natural inputs. Our circuits simultaneously support a suite of four basic kinds of task, each requiring some circuit modification: hierarchical memory formation, pairwise association, supervised memorization, and inductive learning of threshold functions. The capacity of our circuits is established by experiments in which sequences of several thousand such actions are simulated by computer and the circuits created tested for subsequent efficacy. Our underlying theory is apparently the only biologically plausible systems-level theory of learning and memory in cortex for which such a demonstration has been performed, and we argue that no general theory of information processing in the brain can be considered viable without such a demonstration.

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Du, Xuefeng, Haohan Wang, Zhenxi Zhu, Xiangrui Zeng, Yi-Wei Chang, Jing Zhang, Eric Xing, and Min Xu. "Active learning to classify macromolecular structures in situ for less supervision in cryo-electron tomography." Bioinformatics 37, no. 16 (February 23, 2021): 2340–46. http://dx.doi.org/10.1093/bioinformatics/btab123.

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Abstract Motivation Cryo-Electron Tomography (cryo-ET) is a 3D bioimaging tool that visualizes the structural and spatial organization of macromolecules at a near-native state in single cells, which has broad applications in life science. However, the systematic structural recognition and recovery of macromolecules captured by cryo-ET are difficult due to high structural complexity and imaging limits. Deep learning-based subtomogram classification has played critical roles for such tasks. As supervised approaches, however, their performance relies on sufficient and laborious annotation on a large training dataset. Results To alleviate this major labeling burden, we proposed a Hybrid Active Learning (HAL) framework for querying subtomograms for labeling from a large unlabeled subtomogram pool. Firstly, HAL adopts uncertainty sampling to select the subtomograms that have the most uncertain predictions. This strategy enforces the model to be aware of the inductive bias during classification and subtomogram selection, which satisfies the discriminativeness principle in AL literature. Moreover, to mitigate the sampling bias caused by such strategy, a discriminator is introduced to judge if a certain subtomogram is labeled or unlabeled and subsequently the model queries the subtomogram that have higher probabilities to be unlabeled. Such query strategy encourages to match the data distribution between the labeled and unlabeled subtomogram samples, which essentially encodes the representativeness criterion into the subtomogram selection process. Additionally, HAL introduces a subset sampling strategy to improve the diversity of the query set, so that the information overlap is decreased between the queried batches and the algorithmic efficiency is improved. Our experiments on subtomogram classification tasks using both simulated and real data demonstrate that we can achieve comparable testing performance (on average only 3% accuracy drop) by using less than 30% of the labeled subtomograms, which shows a very promising result for subtomogram classification task with limited labeling resources. Availability and implementation https://github.com/xulabs/aitom. Supplementary information Supplementary data are available at Bioinformatics online.

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Gui, Yong, Ronggui Huang, and Yi Ding. "Three faces of the online leftists: An exploratory study based on case observations and big-data analysis." Chinese Journal of Sociology 6, no. 1 (January 2020): 67–101. http://dx.doi.org/10.1177/2057150x19896537.

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Left-leaning social thoughts are not a unitary and coherent theoretical system, and leftists can be divided into divergent groups. Based on inductive qualitative observations, this article proposes a theoretical typology of two dimensions of theoretical resources and position orientations to describe left-wing social thoughts communicated in online space. Empirically, we used a mixed approach, an integration of case observations and big-data analyses of Weibo tweets, to investigate three types of left-leaning social thoughts. The identified left-leaning social thoughts include state-centered leftism, populist leftism, and liberal leftism, which are consistent with the proposed theoretical typology. State-centered leftism features strong support of the state and the current regime and a negative attitude toward the West, populist leftism is characterized by unequivocal affirmation of the revolutionary legacy and support for disadvantaged grassroots, and liberal leftism harbors a grassroots position and a decided affirmation of individual rights. In addition, we used supervised machine learning and social network analysis techniques to identify online communities that harbor the afore-mentioned left-leaning social thoughts and analyzed the interaction patterns within and across communities as well as the evolutions of community structures. We found that during the study period of 2012–2014, the liberal leftists gradually declined and the corresponding communities dissolved; the interactions between populist leftists and state-centered leftists intensified, and the ideational cleavage between these two camps increased the online confrontations. This article demonstrates that the mixed method approach of integrating traditional methods with big-data analytics has enormous potential in the sub-discipline of digital sociology.

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Kalibhat, Neha Mukund, Yogesh Balaji, and Soheil Feizi. "Winning Lottery Tickets in Deep Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 8038–46. http://dx.doi.org/10.1609/aaai.v35i9.16980.

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The lottery ticket hypothesis suggests that sparse, sub-networks of a given neural network, if initialized properly, can be trained to reach comparable or even better performance to that of the original network. Prior works in lottery tickets have primarily focused on the supervised learning setup, with several papers proposing effective ways of finding winning tickets in classification problems. In this paper, we confirm the existence of winning tickets in deep generative models such as GANs and VAEs. We show that the popular iterative magnitude pruning approach (with late resetting) can be used with generative losses to find the winning tickets. This approach effectively yields tickets with sparsity up to 99% for AutoEncoders, 93% for VAEs and 89% for GANs on CIFAR and Celeb-A datasets. We also demonstrate the transferability of winning tickets across different generative models (GANs and VAEs) sharing the same architecture, suggesting that winning tickets have inductive biases that could help train a wide range of deep generative models. Furthermore, we show the practical benefits of lottery tickets in generative models by detecting tickets at very early stages in training called early-bird tickets. Through early-bird tickets, we can achieve up to 88% reduction in floating-point operations (FLOPs) and 54% reduction in training time, making it possible to train large-scale generative models over tight resource constraints. These results out-perform existing early pruning methods like SNIP (Lee, Ajanthan, and Torr 2019) and GraSP(Wang, Zhang, and Grosse 2020). Our findings shed light towards existence of proper network initializations that could improve convergence and stability of generative models.

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Villanacci, V., T. L. Parigi, R. Del Amor, P. Mesguer Esbrì, X. Gui, A. Bazarova, P. Bhandari, et al. "OP15 A new simplified histology artificial intelligence system for accurate assessment of remission in Ulcerative Colitis." Journal of Crohn's and Colitis 16, Supplement_1 (January 1, 2022): i015—i017. http://dx.doi.org/10.1093/ecco-jcc/jjab232.014.

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Abstract Background Histological remission (HR) is evolving as a treatment target in ulcerative colitis (UC). Several histological indices have been developed, however, their widespread adoption beyond clinical trials is limited by practical difficulties and interobserver variability. Furthermore, the relative complexity of available scores hinders the development of an AI algorithm. We aimed to develop a simple histologic index, aligned to endoscopy, and apply it to a computer-aided diagnosis (CAD) system to evaluate HR. Methods 614 digitalised biopsies (WSI) from 307 UC patients enrolled into a prospective multicentre study1 were analysed. First, the simple PICASSO Histologic Remission Index (PHRI) based only on the presence of neutrophils, was developed and validated by expert pathologists. Table 1 To implement PHRI in a CAD system we designed a semi-supervised inductive transfer learning strategy composed of two modules. The first consists of a novel deep learning strategy based on a convolutional neural network architecture. This detected neutrophils in areas (patches) of a subset of 314 biopsies (172 remission, 142 active), in 158 of which the presence of neutrophils had been meticulously annotated at pixel level. WSI were then divided in training (172), validation (47), and testing sets (95). Following a multiple instance learning paradigm, a second model combined the features of all patches of each biopsy into a dichotomous result: presence/absence of disease activity. Figure 1 and 2. Finally, we compared the AI prediction with the pathologists’ assessment. Results PHRI correlated strongly with all endoscopic scores (MES, UCEIS and PICaSSO) of the same bowel areas (rectum and sigmoid) (Spearman’s ρ= 0.55 to 0.78). Inter-reader agreement between pathologists was almost perfect (ICC 0.84). In the validation and testing sets our model predicted the presence of neutrophils respectively with 61% and 72% sensitivity, 98% and 84% specificity, 93% and 75% positive predictive value (PPV), and 86% and 83% negative predictive value (NPV) respectively (Table 2). When predicting remission in whole biopsies, in the validation cohort the AI system had 65% sensitivity, 93% specificity, 86% PPV, and 78% NPV. In the testing cohort the same metrics were 62%, 94%, 90%, and 73% (Table 2). Conclusion PHRI is the simplest histological index in UC and correlates strongly with endoscopic activity. Based on PHRI we developed the first artificial intelligence model able to accurately predict histological remission in biopsies of UC. This tool can effectively expedite, support, and standardise the histological assessment of UC in clinical practice. Reference 1. Iacucci et al. Gastroenterology 2021

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ROKACH, LIOR, ODED MAIMON, and OMRI ARAD. "IMPROVING SUPERVISED LEARNING BY SAMPLE DECOMPOSITION." International Journal of Computational Intelligence and Applications 05, no. 01 (March 2005): 37–53. http://dx.doi.org/10.1142/s146902680500143x.

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This paper introduces a new ensemble technique, cluster-based concurrent decomposition (CBCD) that induces an ensemble of classifiers by decomposing the training set into mutually exclusive sub-samples of equal-size. The CBCD algorithm first clusters the instance space by using the K-means clustering algorithm. Afterwards it produces disjoint sub-samples using the clusters in such a way that each sub-sample is comprised of tuples from all clusters and hence represents the entire dataset. An induction algorithm is applied in turn to each subset, followed by a voting mechanism that combines the classifier's predictions. The CBCD algorithm has two tuning parameters: the number of clusters and the number of subsets to create. Using a suitable meta-learning it is possible to tune these parameters properly. In the experimental study we conducted, the CBCD algorithm, using an embedded C4.5 algorithm, outperformed the bagging algorithm of the same computational complexity.

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Başkaya, Osman, and David Jurgens. "Semi-supervised Learning with Induced Word Senses for State of the Art Word Sense Disambiguation." Journal of Artificial Intelligence Research 55 (April 22, 2016): 1025–58. http://dx.doi.org/10.1613/jair.4917.

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Word Sense Disambiguation (WSD) aims to determine the meaning of a word in context, and successful approaches are known to benefit many applications in Natural Language Processing. Although supervised learning has been shown to provide superior WSD performance, current sense-annotated corpora do not contain a sufficient number of instances per word type to train supervised systems for all words. While unsupervised techniques have been proposed to overcome this data sparsity problem, such techniques have not outperformed supervised methods. In this paper, we propose a new approach to building semi-supervised WSD systems that combines a small amount of sense-annotated data with information from Word Sense Induction, a fully-unsupervised technique that automatically learns the different senses of a word based on how it is used. In three experiments, we show how sense induction models may be effectively combined to ultimately produce high-performance semi-supervised WSD systems that exceed the performance of state-of-the-art supervised WSD techniques trained on the same sense-annotated data. We anticipate that our results and released software will also benefit evaluation practices for sense induction systems and those working in low-resource languages by demonstrating how to quickly produce accurate WSD systems with minimal annotation effort.

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Perryman, Kristi, Erin Popejoy, and Anthony Suarez. "Using the Enneagram to Facilitate the Supervision Relationship: A Qualitative Study." Journal of Counseling Research and Practice 3, no. 1 (April 1, 2018): 16–30. http://dx.doi.org/10.56702/uckx8598/jcrp0301.2.

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A phenomenological study was conducted to gain knowledge of the lived experience of three master’s level counseling supervisees, with a doctoral student supervisor, utilizing the Enneagram, an ancient personality classification system of nine core personality types, throughout 15 weeks of supervision. This study explored the use of the Enneagram and its impact on the supervisory relationship. Emergent themes included: (a) self-awareness; (b) other awareness; (c) relationship and rapport; and (d) professional identity and role induction. The use of the Enneagram within supervision during early stages of counselor development appeared to be helpful to these students in fostering growth and learning.

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Perryman, Kristi, Erin Popejoy, and Anthony Suarez. "Using the Enneagram to Facilitate the Supervision Relationship: A Qualitative Study." Journal of Counseling Research and Practice 3, no. 1 (April 1, 2018): 16–30. http://dx.doi.org/10.56702/hcfr5704.

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A phenomenological study was conducted to gain knowledge of the lived experience of three master’s level counseling supervisees, with a doctoral student supervisor, utilizing the Enneagram, an ancient personality classification system of nine core personality types, throughout 15 weeks of supervision. This study explored the use of the Enneagram and its impact on the supervisory relationship. Emergent themes included: (a) self-awareness; (b) other awareness; (c) relationship and rapport; and (d) professional identity and role induction. The use of the Enneagram within supervision during early stages of counselor development appeared to be helpful to these students in fostering growth and learning.

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Jigyasu, R., V. Shrivastava, and S. Singh. "Prognostics and health management of induction motor by supervised learning classifiers." IOP Conference Series: Materials Science and Engineering 1168, no. 1 (July 1, 2021): 012006. http://dx.doi.org/10.1088/1757-899x/1168/1/012006.

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Lee, Keon Myung, Kyoung Soon Hwang, Kyung Mi Lee, Seung Kee Han, Woo Hyun Jung, and Seungbok Lee. "Supervised Learning-Based Feature Selection for Mondrian Paintings Style Authentication." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 7 (November 20, 2012): 894–99. http://dx.doi.org/10.20965/jaciii.2012.p0894.

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This paper concerns feature selection for computational analysis in authenticating works of art. The various features designed and extracted from art work in art forgery detection or the identification of the characteristics of art work style are valuable only when they have a meaningful influence on a given task such as classification. This paper presents features applicable to authenticating the painting style of Piet Mondrian and demonstrates meaningful features by using two supervised learning algorithms, a decision tree induction algorithm C4.5 and the Feature Generating Machine (FGM), both of which are used to select important features in the course of learning.

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Kendale, Samir, Prathamesh Kulkarni, Andrew D. Rosenberg, and Jing Wang. "Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension." Anesthesiology 129, no. 4 (October 1, 2018): 675–88. http://dx.doi.org/10.1097/aln.0000000000002374.

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AbstractEditor’s PerspectiveWhat We Already Know about This TopicWhat This Article Tells Us That Is NewBackgroundHypotension is a risk factor for adverse perioperative outcomes. Machine-learning methods allow large amounts of data for development of robust predictive analytics. The authors hypothesized that machine-learning methods can provide prediction for the risk of postinduction hypotension.MethodsData was extracted from the electronic health record of a single quaternary care center from November 2015 to May 2016 for patients over age 12 that underwent general anesthesia, without procedure exclusions. Multiple supervised machine-learning classification techniques were attempted, with postinduction hypotension (mean arterial pressure less than 55 mmHg within 10 min of induction by any measurement) as primary outcome, and preoperative medications, medical comorbidities, induction medications, and intraoperative vital signs as features. Discrimination was assessed using cross-validated area under the receiver operating characteristic curve. The best performing model was tuned and final performance assessed using split-set validation.ResultsOut of 13,323 cases, 1,185 (8.9%) experienced postinduction hypotension. Area under the receiver operating characteristic curve using logistic regression was 0.71 (95% CI, 0.70 to 0.72), support vector machines was 0.63 (95% CI, 0.58 to 0.60), naive Bayes was 0.69 (95% CI, 0.67 to 0.69), k-nearest neighbor was 0.64 (95% CI, 0.63 to 0.65), linear discriminant analysis was 0.72 (95% CI, 0.71 to 0.73), random forest was 0.74 (95% CI, 0.73 to 0.75), neural nets 0.71 (95% CI, 0.69 to 0.71), and gradient boosting machine 0.76 (95% CI, 0.75 to 0.77). Test set area for the gradient boosting machine was 0.74 (95% CI, 0.72 to 0.77).ConclusionsThe success of this technique in predicting postinduction hypotension demonstrates feasibility of machine-learning models for predictive analytics in the field of anesthesiology, with performance dependent on model selection and appropriate tuning.

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Wang, Dingquan, and Jason Eisner. "Fine-Grained Prediction of Syntactic Typology: Discovering Latent Structure with Supervised Learning." Transactions of the Association for Computational Linguistics 5 (December 2017): 147–61. http://dx.doi.org/10.1162/tacl_a_00052.

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We show how to predict the basic word-order facts of a novel language given only a corpus of part-of-speech (POS) sequences. We predict how often direct objects follow their verbs, how often adjectives follow their nouns, and in general the directionalities of all dependency relations. Such typological properties could be helpful in grammar induction. While such a problem is usually regarded as unsupervised learning, our innovation is to treat it as supervised learning, using a large collection of realistic synthetic languages as training data. The supervised learner must identify surface features of a language’s POS sequence (hand-engineered or neural features) that correlate with the language’s deeper structure (latent trees). In the experiment, we show: 1) Given a small set of real languages, it helps to add many synthetic languages to the training data. 2) Our system is robust even when the POS sequences include noise. 3) Our system on this task outperforms a grammar induction baseline by a large margin.

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Nurkholis, Nurkholis. "Peran Kepala Sekolah dalam Supervisi Pendidikan." INSANIA : Jurnal Pemikiran Alternatif Kependidikan 26, no. 2 (December 31, 2021): 306–21. http://dx.doi.org/10.24090/insania.v26i2.5612.

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This paper aims to explain the role of principals in educational supervision. The principal has a role in quality development, school progress, improvement, approaches to learning methods and techniques, curriculum development, and creating a conducive and integrative learning climate. Educational supervision is currently developing very rapidly, thus encouraging the acceleration of science and information technology growth. Therefore, education must improve the quality of learning in schools. Schools as formal educational institutions must develop all the potential possessed by students. This research is library research with data sources in books or manuscripts related to school principals' leadership and educational supervision. This research is library research with data sources in manuscripts or books containing leadership principals in academic management. This research uses documentation data collection techniques and inductive data analysis. These findings indicate that the principal's leadership in educational supervision plays a central role in building school organizations to realize quality education.

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Okada, Hugo Kenji Rodrigues, Andre Ricardo Nascimento das Neves, and Ricardo Shitsuka. "Analysis of Decision Tree Induction Algorithms." Research, Society and Development 8, no. 11 (August 24, 2019): e298111473. http://dx.doi.org/10.33448/rsd-v8i11.1473.

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Decision trees are data structures or computational methods that enable nonparametric supervised machine learning and are used in classification and regression tasks. The aim of this paper is to present a comparison between the decision tree induction algorithms C4.5 and CART. A quantitative study is performed in which the two methods are compared by analyzing the following aspects: operation and complexity. The experiments presented practically equal hit percentages in the execution time for tree induction, however, the CART algorithm was approximately 46.24% slower than C4.5 and was considered to be more effective.

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Edgerton, Mary E., Douglas H. Fisher, Lianhong Tang, Lewis J. Frey, and Zhihua Chen. "Data Mining for Gene Networks Relevant to Poor Prognosis in Lung Cancer via Backward-Chaining Rule Induction." Cancer Informatics 3 (January 2007): 117693510700300. http://dx.doi.org/10.1177/117693510700300016.

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We use Backward Chaining Rule Induction (BCRI), a novel data mining method for hypothesizing causative mechanisms, to mine lung cancer gene expression array data for mechanisms that could impact survival. Initially, a supervised learning system is used to generate a prediction model in the form of “IF <conditions> THEN <outcome>” style rules. Next, each antecedent (i.e. an IF condition) of a previously discovered rule becomes the outcome class for subsequent application of supervised rule induction. This step is repeated until a termination condition is satisfied. “Chains” of rules are created by working backward from an initial condition (e.g. survival status). Through this iterative process of “backward chaining,” BCRI searches for rules that describe plausible gene interactions for subsequent validation. Thus, BCRI is a semi-supervised approach that constrains the search through the vast space of plausible causal mechanisms by using a top-level outcome to kick-start the process. We demonstrate the general BCRI task sequence, how to implement it, the validation process, and how BCRI-rules discovered from lung cancer microarray data can be combined with prior knowledge to generate hypotheses about functional genomics.

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Aldarmaki, Hanan, Mahesh Mohan, and Mona Diab. "Unsupervised Word Mapping Using Structural Similarities in Monolingual Embeddings." Transactions of the Association for Computational Linguistics 6 (December 2018): 185–96. http://dx.doi.org/10.1162/tacl_a_00014.

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Most existing methods for automatic bilingual dictionary induction rely on prior alignments between the source and target languages, such as parallel corpora or seed dictionaries. For many language pairs, such supervised alignments are not readily available. We propose an unsupervised approach for learning a bilingual dictionary for a pair of languages given their independently-learned monolingual word embeddings. The proposed method exploits local and global structures in monolingual vector spaces to align them such that similar words are mapped to each other. We show empirically that the performance of bilingual correspondents that are learned using our proposed unsupervised method is comparable to that of using supervised bilingual correspondents from a seed dictionary.

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Journal articles on the topic 'Inductive supervised learning'

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