Relevant bibliographies by topics / Metric machine

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Metric machine'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Metric machine"

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Oshanova, E. S., and D. A. Ponosova. "THE ANALISYS OF NEURAL MACHINE TRANSLATION SYSTEMS USING AUTOMATED MACHINE TRANSLATION EVALUATION METRICS." Social’no-ekonomiceskoe upravlenie: teoria i praktika 20, no. 4 (2024): 106–16. https://doi.org/10.22213/2618-9763-2024-4-106-116.

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This paper introduces current-day automated metrics for neural machine translation evaluation and discusses two ways of result evaluation, which include evaluation using specific automated metrics as well as evaluation by an expert translator. Neural machine translation systems were tested for analysis and evaluation purposes. Google Translate and DeepL Translate systems, which apply a neural network approach, are presented as candidates. The following metrics were considered for evaluation: METEOR as a traditional reference-based metric, COMET as a neural reference-based metric, and COMET-kiwi as a neural reference-free metric. It can be noted that even if these metrics are automated, there is a huge human impact in this automation. Even models with a neural network approach are trained on data provided by humans, as today it is impossible to get rid of reference translations or quality estimations made by experts. Metrics allow to understand and better investigate machine translation, its features and limitations, as well as to determine the direction of its development. As a part of the analysis, a piece of source text was selected for translation, translated into the target languages using selected neural machine translation systems to get candidate translations, and then a reference translation was specified for each of them. The results of the metrics evaluation were helpful for understanding how close machine translation is to human translation, and also helped to learn more about the current stage of machine translation systems development. Expert evaluation helped to understand how well such systems are doing in terms of translation performance.
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Yadav, Dipendra Prasad, Nand Kishor Kumar, and Suresh Kumar Sahani. "Distance Metrics for Machine Learning and it's Relation with Other Distances." Mikailalsys Journal of Mathematics and Statistics 1, no. 1 (2023): 15–23. http://dx.doi.org/10.58578/mjms.v1i1.1990.

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In machine learning, distance metrics play a crucial role in measuring the degree of dissimilarity among data points. When creating and optimizing machine learning models, data scientists and machine learning practitioners can make more informed choices by understanding the features of popular distance metrics and their relationships. The effectiveness and interpretability of the model's output can be greatly influenced by selecting the appropriate distance metric. We explain distance metrics and their relevance in machine learning with various examples of metrics, including Minkowski distance, Manhattan distance, Max Metric for R^n, Taxicab distance, Relative distance, and Hamming distance.
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Golz, Martin, Sebastian Thomas, and Adolf Schenka. "EEG-Based Classification of the Driver Alertness State." Current Directions in Biomedical Engineering 6, no. 3 (2020): 353–56. http://dx.doi.org/10.1515/cdbme-2020-3091.

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AbstractGMLVQ (Generalized Matrix Relevance Learning Vector Quantization) is a method of machine learning with an adaptive metric. While training, the prototype vectors as well as the weight matrix of the metric are adapted simultaneously. The method is presented in more detail and compared with other machine learning methods employing a fixed metric. It was investigated how accurately the methods can assign the 6-channel EEG of 25 young drivers, who drove overnight in the simulation lab, to the two classes of mild and severe drowsiness. Results of cross-validation show that GMLVQ is at 81.7 ± 1.3 % mean classification accuracy. It is not as accurate as support-vector machines (SVM) and gradient boosting machines (GBM) and cannot exploit the potential of learning adaptive metrics in the case of EEG data. However, information is provided on the relevance of each signal feature from the weighting matrix.
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Mahmood, Salman, Raza Hasan, Nor Adnan Yahaya, Saqib Hussain, and Muzammil Hussain. "Evaluation of the Omni-Secure Firewall System in a Private Cloud Environment." Knowledge 4, no. 2 (2024): 141–70. http://dx.doi.org/10.3390/knowledge4020008.

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This research explores the optimization of firewall systems within private cloud environments, specifically focusing on a 30-day evaluation of the Omni-Secure Firewall. Employing a multi-metric approach, the study introduces an innovative effectiveness metric (E) that amalgamates precision, recall, and redundancy considerations. The evaluation spans various machine learning models, including random forest, support vector machines, neural networks, k-nearest neighbors, decision tree, stochastic gradient descent, naive Bayes, logistic regression, gradient boosting, and AdaBoost. Benchmarking against service level agreement (SLA) metrics showcases the Omni-Secure Firewall’s commendable performance in meeting predefined targets. Noteworthy metrics include acceptable availability, target response time, efficient incident resolution, robust event detection, a low false-positive rate, and zero data-loss incidents, enhancing the system’s reliability and security, as well as user satisfaction. Performance metrics such as prediction latency, CPU usage, and memory consumption further highlight the system’s functionality, efficiency, and scalability within private cloud environments. The introduction of the effectiveness metric (E) provides a holistic assessment based on organizational priorities, considering precision, recall, F1 score, throughput, mitigation time, rule latency, and redundancy. Evaluation across machine learning models reveals variations, with random forest and support vector machines exhibiting notably high accuracy and balanced precision and recall. In conclusion, while the Omni-Secure Firewall System demonstrates potential, inconsistencies across machine learning models underscore the need for optimization. The dynamic nature of private cloud environments necessitates continuous monitoring and adjustment of security systems to fully realize benefits while safeguarding sensitive data and applications. The significance of this study lies in providing insights into optimizing firewall systems for private cloud environments, offering a framework for holistic security assessment and emphasizing the need for robust, reliable firewall systems in the dynamic landscape of private clouds. Study limitations, including the need for real-world validation and exploration of advanced machine learning models, set the stage for future research directions.
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Saha, Soumadeep, Utpal Garain, Arijit Ukil, Arpan Pal, and Sundeep Khandelwal. "MedTric : A clinically applicable metric for evaluation of multi-label computational diagnostic systems." PLOS ONE 18, no. 8 (2023): e0283895. http://dx.doi.org/10.1371/journal.pone.0283895.

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When judging the quality of a computational system for a pathological screening task, several factors seem to be important, like sensitivity, specificity, accuracy, etc. With machine learning based approaches showing promise in the multi-label paradigm, they are being widely adopted to diagnostics and digital therapeutics. Metrics are usually borrowed from machine learning literature, and the current consensus is to report results on a diverse set of metrics. It is infeasible to compare efficacy of computational systems which have been evaluated on different sets of metrics. From a diagnostic utility standpoint, the current metrics themselves are far from perfect, often biased by prevalence of negative samples or other statistical factors and importantly, they are designed to evaluate general purpose machine learning tasks. In this paper we outline the various parameters that are important in constructing a clinical metric aligned with diagnostic practice, and demonstrate their incompatibility with existing metrics. We propose a new metric, MedTric that takes into account several factors that are of clinical importance. MedTric is built from the ground up keeping in mind the unique context of computational diagnostics and the principle of risk minimization, penalizing missed diagnosis more harshly than over-diagnosis. MedTric is a unified metric for medical or pathological screening system evaluation. We compare this metric against other widely used metrics and demonstrate how our system outperforms them in key areas of medical relevance.
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Wang, Chaoyong, Yanfeng Sun, and Yanchun Liang. "An Improved SVM Based on Similarity Metric." JUCS - Journal of Universal Computer Science 13, no. (10) (2007): 1462–70. https://doi.org/10.3217/jucs-013-10-1462.

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A novel support vector machine method for classification is presented in this paper. A modified kernel function based on the similarity metric and Riemannian metric is applied to the support vector machine. In general, it is believed that the similarity of homogeneous samples is higher than that of inhomogeneous samples. Therefore, in Riemannian geometry, Riemannian metric can be used to reflect local property of a curve. In order to enlarge the similarity metric of the homogeneous samples or reduce that of the inhomogeneous samples in the feature space, Riemannian metric is used in the kernel function of the SVM. Simulated experiments are performed using the databases including an artificial and the UCI real data. Simulation results show the effectiveness of the proposed algorithm through the comparison with four typical kernel functions without similarity metric.
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Haner Kırğıl, Elif Nur, and Tülin Erçelebi Ayyıldız. "Predicting Software Cohesion Metrics with Machine Learning Techniques." Applied Sciences 13, no. 6 (2023): 3722. http://dx.doi.org/10.3390/app13063722.

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The cohesion value is one of the important factors used to evaluate software maintainability. However, measuring the cohesion value is a relatively difficult issue when tracing the source code manually. Although there are many static code analysis tools, not every tool measures every metric. The user should apply different tools for different metrics. In this study, besides the use of these tools, we predicted the cohesion values (LCOM2, TCC, LCC, and LSCC) with machine learning techniques (KNN, REPTree, multi-layer perceptron, linear regression (LR), support vector machine, and random forest (RF)) to solve them alternatively. We created two datasets utilizing two different open-source software projects. According to the obtained results, for the LCOM2 and TCC metrics, the KNN algorithm provided the best results, and for LCC and LSCC metrics, the REPTree algorithm was the best. However, out of all the metrics, RF, REPTree, and KNN had close performances with each other, and therefore any of the RF, REPTree, and KNN techniques can be used for software cohesion metric prediction.
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Pankaj Javale, Deepali, and Sharmishta Desai. "Healthcare Critical Diagnosis Accuracy." International journal of electrical and computer engineering systems 14, no. 8 (2023): 927–34. http://dx.doi.org/10.32985/ijeces.14.8.10.

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Since at least a decade, Machine Learning has attracted the interest of researchers. Among the topics of discussion is the application of Machine Learning (ML) and Deep Learning (DL) to the healthcare industry. Several implementations are performed on the medical dataset to verify its precision. The four main players, True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN), play a crucial role in determining the classifier's performance. Various metrics are provided based on the main players. Selecting the appropriate performance metric is a crucial step. In addition to TP and TN, FN should be given greater weight when a healthcare dataset is evaluated for disease diagnosis or detection. Thus, a suitable performance metric must be considered. In this paper, a novel machine learning metric referred to as Healthcare-Critical-Diagnostic-Accuracy (HCDA) is proposed and compared to the well-known metrics accuracy and ROC_AUC score. The machine learning classifiers Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Naive Bayes (NB) are implemented on four distinct datasets. The obtained results indicate that the proposed HCDA metric is more sensitive to FN counts. The results show, that even if there is rise in %FN for dataset 1 to 10.31 % then too accuracy is 83% ad HCDA shows correlated drop to 72.70 %. Similarly, in dataset 2 if %FN rises to 14.80 for LR classifier, accuracy is 78.2 % and HCDA is 63.45 %. Similar kind of results are obtained for dataset 3 and 4 too. More FN counts result in a lower HCDA score, and vice versa. In common exiting metrics such as Accuracy and ROC_AUC score, even as the FN count increases, the score increases, which is misleading. As a result, it can be concluded that the proposed HCDA is a more robust and accurate metric for Critical Healthcare Analysis, as FN conditions for disease diagnosis and detection are taken into account more than TP and TN.
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Sandhya, Aneja, Aneja Nagender, and Kumaraguru Ponnurangam. "Predictive linguistic cues for fake news: a societal artificial intelligence problem." International Journal of Artificial Intelligence (IJ-AI) 11, no. 4 (2022): 1252–60. https://doi.org/10.11591/ijai.v11.i4.pp1252-1260.

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Media news are making a large part of public opinion and, therefore, must not be fake. News on web sites, blogs, and social media must be analyzed before being published. In this paper, we present linguistic characteristics of media news items to differentiate between fake news and real news using machine learning algorithms. Neural fake news generation, headlines created by machines, semantic incongruities in text and image captions generated by machine are other types of fake news problems. These problems use neural networks which mainly control distributional features rather than evidence. We propose applying correlation between features set and class, and correlation among the features to compute correlation attribute evaluation metric and covariance metric to compute variance of attributes over the news items. Features unique, negative, positive, and cardinal numbers with high values on the metrics are observed to provide a high area under the curve (AUC) and F1-score.
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Iantovics, László Barna. "Black-Box-Based Mathematical Modelling of Machine Intelligence Measuring." Mathematics 9, no. 6 (2021): 681. http://dx.doi.org/10.3390/math9060681.

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Current machine intelligence metrics rely on a different philosophy, hindering their effective comparison. There is no standardization of what is machine intelligence and what should be measured to quantify it. In this study, we investigate the measurement of intelligence from the viewpoint of real-life difficult-problem-solving abilities, and we highlight the importance of being able to make accurate and robust comparisons between multiple cooperative multiagent systems (CMASs) using a novel metric. A recent metric presented in the scientific literature, called MetrIntPair, is capable of comparing the intelligence of only two CMASs at an application. In this paper, we propose a generalization of that metric called MetrIntPairII. MetrIntPairII is based on pairwise problem-solving intelligence comparisons (for the same problem, the problem-solving intelligence of the studied CMASs is evaluated experimentally in pairs). The pairwise intelligence comparison is proposed to decrease the necessary number of experimental intelligence measurements. MetrIntPairII has the same properties as MetrIntPair, with the main advantage that it can be applied to any number of CMASs conserving the accuracy of the comparison, while it exhibits enhanced robustness. An important property of the proposed metric is the universality, as it can be applied as a black-box method to intelligent agent-based systems (IABSs) generally, not depending on the aspect of IABS architecture. To demonstrate the effectiveness of the MetrIntPairII metric, we provide a representative experimental study, comparing the intelligence of several CMASs composed of agents specialized in solving an NP-hard problem.
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Dissertations / Theses on the topic "Metric machine"

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Hagen, Erling. "Using machine learning to balance metric trees." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2006. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-10118.

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<p>The emergence of complex data objects that must to be indexed and accessed in databases has created a need for access methods that are both dynamic and efficient. Lately, metric tree structures have become a popular way of handling this because of the advantages they have compared to traditional methods based on spatial indexing. The most common way to handle indexing is to build tree structures and then prune out branches of the trees during search, and for a dynamic indexing structure it is important that these trees stay balanced in order to keep the worst case search time as low as possible. Normally, this is done based on complex criteria and reshuffling operations. Another way to handle balancing is General Balanced Trees (GBT), proposed by Arne Andersson (Journal of Algorithms 30, 1999), which uses simple, global criteria for rebalancing binary search trees by using total and partial rebuilding. This thesis explores if it is possible to apply this to metric tree structures, and especially two static metric tree structures called the Vantage Point Tree and the Multiple Vantage Point Tree. It discusses how to best make these into dynamic tree structures and how to apply balancing by using GBT paradigms on them. The results of the performance of the new tree structures are analyzed, and the results are compared against already existing structures. The results shows that this works for balancing the trees, and that the structures perform reasonably well compared to already existing structures.</p>
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Chang, Hong. "Semi-supervised distance metric learning /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?COMP%202006%20CHANG.

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Cao, Qiong. "Some topics on similarity metric learning." Thesis, University of Exeter, 2015. http://hdl.handle.net/10871/18662.

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The success of many computer vision problems and machine learning algorithms critically depends on the quality of the chosen distance metrics or similarity functions. Due to the fact that the real-data at hand is inherently task- and data-dependent, learning an appropriate distance metric or similarity function from data for each specific task is usually superior to the default Euclidean distance or cosine similarity. This thesis mainly focuses on developing new metric and similarity learning models for three tasks: unconstrained face verification, person re-identification and kNN classification. Unconstrained face verification is a binary matching problem, the target of which is to predict whether two images/videos are from the same person or not. Concurrently, person re-identification handles pedestrian matching and ranking across non-overlapping camera views. Both vision problems are very challenging because of the large transformation differences in images or videos caused by pose, expression, occlusion, problematic lighting and viewpoint. To address the above concerns, two novel methods are proposed. Firstly, we introduce a new dimensionality reduction method called Intra-PCA by considering the robustness to large transformation differences. We show that Intra-PCA significantly outperforms the classic dimensionality reduction methods (e.g. PCA and LDA). Secondly, we propose a novel regularization framework called Sub-SML to learn distance metrics and similarity functions for unconstrained face verifica- tion and person re-identification. The main novelty of our formulation is to incorporate both the robustness of Intra-PCA to large transformation variations and the discriminative power of metric and similarity learning, a property that most existing methods do not hold. Working with the task of kNN classification which relies a distance metric to identify the nearest neighbors, we revisit some popular existing methods for metric learning and develop a general formulation called DMLp for learning a distance metric from data. To obtain the optimal solution, a gradient-based optimization algorithm is proposed which only needs the computation of the largest eigenvector of a matrix per iteration. Although there is a large number of studies devoted to metric/similarity learning based on different objective functions, few studies address the generalization analysis of such methods. We describe a novel approch for generalization analysis of metric/similarity learning which can deal with general matrix regularization terms including the Frobenius norm, sparse L1-norm, mixed (2, 1)-norm and trace-norm. The novel models developed in this thesis are evaluated on four challenging databases: the Labeled Faces in the Wild dataset for unconstrained face verification in still images; the YouTube Faces database for video-based face verification in the wild; the Viewpoint Invariant Pedestrian Recognition database for person re-identification; the UCI datasets for kNN classification. Experimental results show that the proposed methods yield competitive or state-of-the-art performance.
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Ruan, Yang. "Smooth and locally linear semi-supervised metric learning /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20RUAN.

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Shi, Bibo. "Diversification and Generalization for Metric Learning with Applications in Neuroimaging." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1448980736.

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Engström, Isak. "Automated Gait Analysis : Using Deep Metric Learning." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178139.

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Sectors of security, safety, and defence require methods for identifying people on the individual level. Automation of these tasks has the potential of outperforming manual labor, as well as relieving workloads. The ever-extending surveillance camera networks, advances in human pose estimation from monocular cameras, together with the progress of deep learning techniques, pave the way for automated walking gait analysis as an identification method. This thesis investigates the use of 2D kinematic pose sequences to represent gait, monocularly extracted from a limited dataset containing walking individuals captured from five camera views. The sequential information of the gait is captured using recurrent neural networks. Techniques in deep metric learning are applied to evaluate two network models, with contrasting output dimensionalities, against deep-metric-, and non-deep-metric-based embedding spaces. The results indicate that the gait representation, network designs, and network learning structure show promise when identifying individuals, scaling particularly well to unseen individuals. However, with the limited dataset, the network models performed best when the dataset included the labels from both the individuals and the camera views simultaneously, contrary to when the data only contained the labels from the individuals without the information of the camera views. For further investigations, an extension of the data would be required to evaluate the accuracy and effectiveness of these methods, for the re-identification task of each individual.<br><p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>
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Witoszko, Izabela. "How and why robotics automate work : analyzing automation of tasks using machine learning suitability assessment metric." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118508.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 86-89).<br>As we are at the beginning of the Second Machine Age, where Al, Machine Learning, and Robotics technologies are increasingly influencing this revolution, we are experiencing significant automation changes in many industries such as warehousing and distribution centers. Many of the jobs in these industries aren't just being transformed but also partially or fully automated, often replacing the lowest skilled workers. Even though the core technologies driving automation today are improving exponentially, there are still many areas where human workers exceed and thrive. Some of the jobs might be automated, but there are some tasks which prove to be difficult for machines to perform. The research tries to understand how technology is automating tasks within warehousing jobs right now? By applying rigorous metrics, developed by Erik Brynjolfsson and Tom Mitchell to jobs within warehouses, the thesis aims to show which tasks within these jobs have the highest suitability for machine learning and robotics automation. The research includes the analysis of the not automated tasks and the possible reasons and opportunities for automation.<br>by Izabela Witoszko.<br>S.M. in Engineering and Management
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Carriere, Mathieu. "On Metric and Statistical Properties of Topological Descriptors for geometric Data." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS433/document.

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Dans le cadre de l'apprentissage automatique, l'utilisation de représentations alternatives, ou descripteurs, pour les données est un problème fondamental permettant d'améliorer sensiblement les résultats des algorithmes. Parmi eux, les descripteurs topologiques calculent et encodent l'information de nature topologique contenue dans les données géométriques. Ils ont pour avantage de bénéficier de nombreuses bonnes propriétés issues de la topologie, et désirables en pratique, comme par exemple leur invariance aux déformations continues des données. En revanche, la structure et les opérations nécessaires à de nombreuses méthodes d'apprentissage, comme les moyennes ou les produits scalaires, sont souvent absents de l'espace de ces descripteurs. Dans cette thèse, nous étudions en détail les propriétés métriques et statistiques des descripteurs topologiques les plus fréquents, à savoir les diagrammes de persistance et Mapper. En particulier, nous montrons que le Mapper, qui est empiriquement un descripteur instable, peut être stabilisé avec une métrique appropriée, que l'on utilise ensuite pour calculer des régions de confiance et pour régler automatiquement ses paramètres. En ce qui concerne les diagrammes de persistance, nous montrons que des produits scalaires peuvent être utilisés via des méthodes à noyaux, en définissant deux noyaux, ou plongements, dans des espaces de Hilbert en dimension finie et infinie<br>In the context of supervised Machine Learning, finding alternate representations, or descriptors, for data is of primary interest since it can greatly enhance the performance of algorithms. Among them, topological descriptors focus on and encode the topological information contained in geometric data. One advantage of using these descriptors is that they enjoy many good and desireable properties, due to their topological nature. For instance, they are invariant to continuous deformations of data. However, the main drawback of these descriptors is that they often lack the structure and operations required by most Machine Learning algorithms, such as a means or scalar products. In this thesis, we study the metric and statistical properties of the most common topological descriptors, the persistence diagrams and the Mappers. In particular, we show that the Mapper, which is empirically instable, can be stabilized with an appropriate metric, that we use later on to conpute confidence regions and automatic tuning of its parameters. Concerning persistence diagrams, we show that scalar products can be defined with kernel methods by defining two kernels, or embeddings, into finite and infinite dimensional Hilbert spaces
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Neo, TohKoon. "A Direct Algorithm for the K-Nearest-Neighbor Classifier via Local Warping of the Distance Metric." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2168.pdf.

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Bellet, Aurélien. "Supervised metric learning with generalization guarantees." Phd thesis, Université Jean Monnet - Saint-Etienne, 2012. http://tel.archives-ouvertes.fr/tel-00770627.

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In recent years, the crucial importance of metrics in machine learningalgorithms has led to an increasing interest in optimizing distanceand similarity functions using knowledge from training data to make them suitable for the problem at hand.This area of research is known as metric learning. Existing methods typically aim at optimizing the parameters of a given metric with respect to some local constraints over the training sample. The learned metrics are generally used in nearest-neighbor and clustering algorithms.When data consist of feature vectors, a large body of work has focused on learning a Mahalanobis distance, which is parameterized by a positive semi-definite matrix. Recent methods offer good scalability to large datasets.Less work has been devoted to metric learning from structured objects (such as strings or trees), because it often involves complex procedures. Most of the work has focused on optimizing a notion of edit distance, which measures (in terms of number of operations) the cost of turning an object into another.We identify two important limitations of current supervised metric learning approaches. First, they allow to improve the performance of local algorithms such as k-nearest neighbors, but metric learning for global algorithms (such as linear classifiers) has not really been studied so far. Second, and perhaps more importantly, the question of the generalization ability of metric learning methods has been largely ignored.In this thesis, we propose theoretical and algorithmic contributions that address these limitations. Our first contribution is the derivation of a new kernel function built from learned edit probabilities. Unlike other string kernels, it is guaranteed to be valid and parameter-free. Our second contribution is a novel framework for learning string and tree edit similarities inspired by the recent theory of (epsilon,gamma,tau)-good similarity functions and formulated as a convex optimization problem. Using uniform stability arguments, we establish theoretical guarantees for the learned similarity that give a bound on the generalization error of a linear classifier built from that similarity. In our third contribution, we extend the same ideas to metric learning from feature vectors by proposing a bilinear similarity learning method that efficiently optimizes the (epsilon,gamma,tau)-goodness. The similarity is learned based on global constraints that are more appropriate to linear classification. Generalization guarantees are derived for our approach, highlighting that our method minimizes a tighter bound on the generalization error of the classifier. Our last contribution is a framework for establishing generalization bounds for a large class of existing metric learning algorithms. It is based on a simple adaptation of the notion of algorithmic robustness and allows the derivation of bounds for various loss functions and regularizers.
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Books on the topic "Metric machine"

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Walsh, Ronald A. McGraw-Hill machining and metalworking handbook. McGraw-Hill, 1994.

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Walsh, Ronald A. McGraw-Hill machining and metalworking handbook. 3rd ed. McGraw-Hill, 2006.

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Walsh, Ronald A. McGraw-Hill machining and metalworking handbook. 3rd ed. McGraw-Hill, 2006.

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Institute, American National Standards. Shaft and housing fits for metric radial ball and roller bearings (except tapered roller bearings) conforming to basic boundary plans. Anti-Friction Bearing Manufacturers Association, 1988.

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Stoecker, W. F. SI units for the HVAC/R professional. Business News Pub. Co., 1992.

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Society for Machinery Failure Prevention Technology. Meeting. Metrics: The key to success : proceedings of the 60th Meeting of the Society for Machinery Failure Prevention Technology, Virginia Beach, Virginia, April 3-6, 2006. Society for Machinery Failure Prevention Technology (MFPT), 2006.

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Kulis, Brian. Metric Learning: A Review. Now Publishers, 2013.

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Machine Dowel Pins - Hardened Ground (Metric Series). Amer Society of Mechanical, 1994.

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Patil, H. G., and S. C. Pilli. Machine Design Data Handbook (S. I. Metric). I.K. International Publishing House Pvt. Ltd, 2014.

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Lock, D. S. Engineers' Metric Data Manual and Buyers' Guide. Elsevier Science & Technology Books, 2013.

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Book chapters on the topic "Metric machine"

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Zhou, Zhi-Hua. "Dimensionality Reduction and Metric Learning." In Machine Learning. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-1967-3_10.

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Webb, Geoffrey I., Claude Sammut, Claudia Perlich, et al. "Local Distance Metric Adaptation." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_484.

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Cao, Qiong, Yiming Ying, and Peng Li. "Distance Metric Learning Revisited." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33460-3_24.

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Feng, Jianqiao, Xiaoying Tang, Minh Tang, Carey Priebe, and Michael Miller. "Metric Space Structures for Computational Anatomy." In Machine Learning in Medical Imaging. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02267-3_16.

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Gutiérrez-Naranjo, Miguel A., José A. Alonso-Jiménez, and Joaquín Borrego-Díaz. "A Quasi-Metric for Machine Learning." In Advances in Artificial Intelligence — IBERAMIA 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36131-6_20.

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Wang, Jun, Adam Woznica, and Alexandros Kalousis. "Learning Neighborhoods for Metric Learning." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33460-3_20.

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Markov, Zdravko, and Ivo Marinchev. "Metric-Based Inductive Learning Using Semantic Height Functions." In Machine Learning: ECML 2000. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45164-1_27.

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Liu, Haoting, Fenggang Xu, Shuo Yang, Weidong Dong, and Shunliang Pan. "Image Quality Evaluation Metric of Brightness Contrast." In Man-Machine-Environment System Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2481-9_32.

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Yang, Peipei, Kaizhu Huang, and Cheng-Lin Liu. "Geometry Preserving Multi-task Metric Learning." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33460-3_47.

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Mirzazadeh, Farzaneh, Martha White, András György, and Dale Schuurmans. "Scalable Metric Learning for Co-Embedding." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23528-8_39.

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Conference papers on the topic "Metric machine"

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Kovacs, Geza, Daniel Deutsch, and Markus Freitag. "Mitigating Metric Bias in Minimum Bayes Risk Decoding." In Proceedings of the Ninth Conference on Machine Translation. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.wmt-1.109.

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Rozanov, Nikolai, Vikentiy Pankov, Dmitrii Mukhutdinov, and Dima Vypirailenko. "IsoChronoMeter: A Simple and Effective Isochronic Translation Evaluation Metric." In Proceedings of the Ninth Conference on Machine Translation. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.wmt-1.29.

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K.S., Midhula, and Arun Raj Kumar P. "Power Metric Based BBR Congestion Control Using XGBoost." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10968478.

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Roy, Anwesha, and Prakash Selvakumar. "Evaluating Text Generation Confidence: A Pseudo Confidence Metric for Completion Models." In 2024 Intelligent Systems and Machine Learning Conference (ISML). IEEE, 2024. https://doi.org/10.1109/isml60050.2024.11007363.

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Chen, Qitong, Hong Zhuang, Yueyuan Zhang, Liang Chen, and Qi Li. "Few-shot Metric Adversarial Adaptation for Cross-machine Fault Diagnosis." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886709.

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Ruiz, A., P. E. Lopez-de-Teruel, and L. Fernandez-Maimo. "Practical Planar Metric Rectification." In British Machine Vision Conference 2006. British Machine Vision Association, 2006. http://dx.doi.org/10.5244/c.20.60.

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Comelles, Elisabet, and Jordi Atserias. "VERTa: a Linguistically-motivated Metric at the WMT15 Metrics Task." In Proceedings of the Tenth Workshop on Statistical Machine Translation. Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/w15-3045.

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Wiley, Anthony G., and Kam W. Wong. "Metric aspects of zoom vision." In Close-Range Photogrammetry Meets Machine Vision. SPIE, 1990. http://dx.doi.org/10.1117/12.2294259.

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Shi, Haibo, Yong Luo, Chao Xu, and Yonggang Wen. "Manifold Regularized Transfer Distance Metric Learning." In British Machine Vision Conference 2015. British Machine Vision Association, 2015. http://dx.doi.org/10.5244/c.29.158.

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Zhang, Kaizhong. "Similarity metric induced metrics with application in machine learning and bioinformatics." In 2016 IEEE 15th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2016. http://dx.doi.org/10.1109/icci-cc.2016.7862048.

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Reports on the topic "Metric machine"

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Maloney, Megan, Sarah Becker, Andrew Griffin, Susan Lyon, and Kristofer Lasko. Automated built-up infrastructure land cover extraction using index ensembles with machine learning, automated training data, and red band texture layers. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49370.

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Automated built-up infrastructure classification is a global need for planning. However, individual indices have weaknesses, including spectral confusion with bare ground, and computational requirements for deep learning are intensive. We present a computationally lightweight method to classify built-up infrastructure. We use an ensemble of spectral indices and a novel red-band texture layer with global thresholds determined from 12 diverse sites (two seasonally varied images per site). Multiple spectral indexes were evaluated using Sentinel-2 imagery. Our texture metric uses the red band to separate built-up infrastructure from spectrally similar bare ground. Our evaluation produced global thresholds by evaluating ground truth points against a range of site-specific optimal index thresholds across the 24 images. These were used to classify an ensemble, and then spectral indexes, texture, and stratified random sampling guided training data selection. The training data fit a random forest classifier to create final binary maps. Validation found an average overall accuracy of 79.95% (±4%) and an F1 score of 0.5304 (±0.07). The inclusion of the texture metric improved overall accuracy by 14–21%. A comparison to site-specific thresholds and a deep learning-derived layer is provided. This automated built-up infrastructure mapping framework requires only public imagery to support time-sensitive land management workflows.
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Brandt, Kierstyn. An Examination of Metrics on Cray Machines: Power Management Database (PMDB) & Lightweight Distributed Metric Service (LDMS). Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1467180.

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Zheng, Zhonghua, Nicole Riemer, Matthew West, and Valentine G. Anantharaj. Evaluation of Machine Learning Approaches to Estimate Aerosol Mixing State Metrics in Atmospheric Models. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1513380.

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Pompeu, Gustavo, and José Luiz Rossi. Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004491.

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The study of the predictability of exchange rates has been a very recurring theme on the economics literature for decades, and very often is not possible to beat a random walk prediction, particularly when trying to forecast short time periods. Although there are several studies about exchange rate forecasting in general, predictions of specifically Brazilian real (BRL) to United States dollar (USD) exchange rates are very hard to find in the literature. The objective of this work is to predict the specific BRL to USD exchange rates by applying machine learning models combined with fundamental theories from macroeconomics, such as monetary and Taylor rule models, and compare the results to those of a random walk model by using the root mean squared error (RMSE) and the Diebold-Mariano (DM) test. We show that it is possible to beat the random walk by these metrics.
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Konsam, Manis Kumar, Amanda Thounajam, Prasad Vaidya, Gopikrishna A, Uthej Dalavai, and Yashima Jain. Machine Learning-Enhanced Control System for Optimized Ceiling Fan and Air Conditioner Operation for Thermal Comfort. Indian Institute for Human Settlements, 2024. http://dx.doi.org/10.24943/mlcsocfacotc6.2023.

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This paper proposes and tests the implementation of a sustainable cooling approach that uses a machine learning model to predict operative temperatures, and an automated control sequence that prioritises ceiling fans over air conditioners. The robustness of the machine learning model (MLM) is tested by comparing its prediction with that of a straight-line model (SLM) using the metrics of Mean Bias Error (MBE) and Root Mean Squared Error (RMSE). This comparison is done across several rooms to see how each prediction method performs when the conditions are different from those of the original room where the model was trained. A control sequence has been developed where the MLM’s prediction of Operative Temperature (OT) is used to adjust the adaptive thermal comfort band for increased air speed delivered by the ceiling fans to maintain acceptable OT. This control sequence is tested over a two-week period in two different buildings by comparing it with a constant air temperature setpoint (24ºC).
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Alonso-Robisco, Andrés, Andrés Alonso-Robisco, José Manuel Carbó, et al. Empowering financial supervision: a SupTech experiment using machine learning in an early warning system. Banco de España, 2025. https://doi.org/10.53479/39320.

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New technologies have made available a vast amount of new data in the form of text, recording an exponentially increasing share of human and corporate behavior. For financial supervisors, the information encoded in text is a valuable complement to the more traditional balance sheet data typically used to track the soundness of financial institutions. In this study, we exploit several natural language processing (NLP) techniques as well as network analysis to detect anomalies in the Spanish corporate system, identifying both idiosyncratic and systemic risks. We use sentiment analysis at the corporate level to detect sentiment anomalies for specific corporations (idiosyncratic risks), while employing a wide range of network metrics to monitor systemic risks. In the realm of supervisory technology (SupTech), anomaly detection in sentiment analysis serves as a proactive tool for financial authorities. By continuously monitoring sentiment trends, SupTech applications can provide early warnings of potential financial distress or systemic risks.
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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted features based on the statistical significance of classical univariate analysis (p&lt;0.05) and extended () 17 features representing power/coherence of different frequency bands, entropy, and interelectrode-based distance. The analysis was performed before and after weight adjustment for imbalanced data (w). Results: 7 subjects and 376 contacts were included. Before optimization, ML algorithms performed comparably employing conventional features (median CS accuracy: 0.89, IQR [0.88-0.9]). After optimization, neural networks outperformed others in means of accuracy (MLP: 0.86), the area under the curve (AUC) (SLPw, MLPw, MLP: 0.91), recall (SLPw: 0.82, MLPw: 0.81), precision (SLPw: 0.84), and F1-scores (SLPw: 0.82). SVM achieved the best specificity performance. Extending the number of features and adjusting the weights improved recall, precision, and F1-scores by 48.27%, 27.15%, and 39.15%, respectively, with gains or no significant losses in specificity and AUC across CS and Function (correlation r=0.71 between the two clinical scenarios in all performance metrics, p&lt;0.001). Interpretation: Computational passive sensorimotor mapping is feasible and reliable. Feature extension and weight adjustments improve the performance and counterbalance the accuracy paradox. Optimized neural networks outperform other ML algorithms even in binary classification tasks. The best-performing models and the MATLAB® routine employed in signal processing are available to the public at (Link 1).
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Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, 2021. http://dx.doi.org/10.46337/210930.

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Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platforms, expediting complex system computations and rigour. Moreover, we introduce novel quantum sensing technologies in our Meteoceanics satellite constellation, providing unprecedented spatiotemporal coverage, resolution and lead, whilst using exclusively sustainable materials and processes across the value chain. Our technologies bring out novel information physical fingerprints of extreme events, with recently proven records in capturing early warning signs for extreme hydro-meteorologic events and seismic events, and do so with unprecedented quantum-grade resolution, robustness, security, speed and fidelity in sensing, processing and communication. Our advances, from Earth to Space, further provide crucial predictive edge and added value to early warning systems of natural hazards and long-term predictions supporting climatic security and action.
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Valencia, Oscar, Juan José Díaz, and Diego A. Parra. Assessing Macro-Fiscal Risk for Latin American and Caribbean Countries. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004530.

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This paper provides a comprehensive early warning system (EWS) that balances the classical signaling approach with the best-realized machine learning (ML) model for predicting fiscal stress episodes. Using accumulated local effects (ALE), we compute a set of thresholds for the most informative variables that drive the correlation between predictors. In addition, to evaluate the main country risks, we propose a leading fiscal risk indicator, highlighting macro, fiscal and institutional attributes. Estimates from different models suggest significant heterogeneity among the most critical variables in determining fiscal risk across countries. While macro variables have higher relevance for advanced countries, fiscal variables were more significant for Latin American and Caribbean (LAC) and emerging economies. These results are consistent under different liquidity-solvency metrics and have deepened since the global financial crisis.
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Mesquita Moreira, Mauricio, Marcelo Dolabella, Kwanghee Ko, et al. Latin America and Korea: Partners for Sustainable Trade and Investment. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004481.

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The relationship between Korea and Latin American and the Caribbean has come a long way. As the two economies embraced trade with one another in the early 1990s, their connection went from being irrelevant to being a wealth machine. Bilateral trade grew at an impressive annual rate of 11.5%reaching a record high in 2021. The trade boom was followed by US$26 billion in investments by Korean firms in the region since 2000. Despite this meteoric rise, lingering trade barriers remain, and new challenges are emerging from a string of disruptive shocks to the global economyprotectionist backlashes; growing and interrelated sanitary, food, energy, and climate crises; and a fast-moving “digital transformation.” This monograph argues that, despite the challenges, both economies have a set of policies, institutions, and comparative advantages that, if reinforced and leveraged by trade and cooperation, can turn these shocks into bilateral and global opportunities for inclusive and sustainable growth.
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