Увійти

Готові списки джерел за темами / Hyperparameter search

Добірка наукової літератури з теми "Hyperparameter search"

Автор: Grafiati

Опубліковано: 23 червня 2021

Оновлено: 17 лютого 2022

Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями

Оберіть тип джерела:

Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Hyperparameter search".

Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.

Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.

Зміст

Статті в журналах
Дисертації
Частини книг
Тези доповідей конференцій

Статті в журналах з теми "Hyperparameter search":

1

Florea, Adrian-Catalin, and Razvan Andonie. "Weighted Random Search for Hyperparameter Optimization." International Journal of Computers Communications & Control 14, no. 2 (April 14, 2019): 154–69. http://dx.doi.org/10.15837/ijccc.2019.2.3514.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

We introduce an improved version of Random Search (RS), used here for hyperparameter optimization of machine learning algorithms. Unlike the standard RS, which generates for each trial new values for all hyperparameters, we generate new values for each hyperparameter with a probability of change. The intuition behind our approach is that a value that already triggered a good result is a good candidate for the next step, and should be tested in new combinations of hyperparameter values. Within the same computational budget, our method yields better results than the standard RS. Our theoretical results prove this statement. We test our method on a variation of one of the most commonly used objective function for this class of problems (the Grievank function) and for the hyperparameter optimization of a deep learning CNN architecture. Our results can be generalized to any optimization problem dened on a discrete domain.

2

Ghawi, Raji, and Jürgen Pfeffer. "Efficient Hyperparameter Tuning with Grid Search for Text Categorization using kNN Approach with BM25 Similarity." Open Computer Science 9, no. 1 (August 8, 2019): 160–80. http://dx.doi.org/10.1515/comp-2019-0011.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

AbstractIn machine learning, hyperparameter tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. Several approaches have been widely adopted for hyperparameter tuning, which is typically a time consuming process. We propose an efficient technique to speed up the process of hyperparameter tuning with Grid Search. We applied this technique on text categorization using kNN algorithm with BM25 similarity, where three hyperparameters need to be tuned. Our experiments show that our proposed technique is at least an order of magnitude faster than conventional tuning.

3

Yang, Eun-Suk, Jong Dae Kim, Chan-Young Park, Hye-Jeong Song, and Yu-Seop Kim. "Hyperparameter tuning for hidden unit conditional random fields." Engineering Computations 34, no. 6 (August 7, 2017): 2054–62. http://dx.doi.org/10.1108/ec-11-2015-0350.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Purpose In this paper, the problem of a nonlinear model – specifically the hidden unit conditional random fields (HUCRFs) model, which has binary stochastic hidden units between the data and the labels – exhibiting unstable performance depending on the hyperparameter under consideration. Design/methodology/approach There are three main optimization search methods for hyperparameter tuning: manual search, grid search and random search. This study shows that HUCRFs’ unstable performance depends on the hyperparameter values used and its performance is based on tuning that draws on grid and random searches. All experiments conducted used the n-gram features – specifically, unigram, bigram, and trigram. Findings Naturally, selecting a list of hyperparameter values based on a researchers’ experience to find a set in which the best performance is exhibited is better than finding it from a probability distribution. Realistically, however, it is impossible to calculate using the parameters in all combinations. The present research indicates that the random search method has a better performance compared with the grid search method while requiring shorter computation time and a reduced cost. Originality/value In this paper, the issues affecting the performance of HUCRF, a nonlinear model with performance that varies depending on the hyperparameters, but performs better than CRF, has been examined.

4

Wen, Long, Xingchen Ye, and Liang Gao. "A new automatic machine learning based hyperparameter optimization for workpiece quality prediction." Measurement and Control 53, no. 7-8 (July 21, 2020): 1088–98. http://dx.doi.org/10.1177/0020294020932347.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Workpiece quality prediction is very important in modern manufacturing industry. However, traditional machine learning methods are very sensitive to their hyperparameters, making the tuning of the machine learning methods essential to improve the prediction performance. Hyperparameter optimization (HPO) approaches are applied attempting to tune hyperparameters, such as grid search and random search. However, the hyperparameters space for workpiece quality prediction model is high dimension and it consists with continuous, combinational and conditional types of hyperparameters, which is difficult to be tuned. In this article, a new automatic machine learning based HPO, named adaptive Tree Pazen Estimator (ATPE), is proposed for workpiece quality prediction in high dimension. In the proposed method, it can iteratively search the best combination of hyperparameters in the automatic way. During the warm-up process for ATPE, it can adaptively adjust the hyperparameter interval to guide the search. The proposed ATPE is tested on sparse stack autoencoder based MNIST and XGBoost based WorkpieceQuality dataset, and the results show that ATPE provides the state-of-the-art performances in high-dimensional space and can search the hyperparameters in reasonable range by comparing with Tree Pazen Estimator, annealing, and random search, showing its potential in the field of workpiece quality prediction.

5

Hinz, Tobias, Nicolás Navarro-Guerrero, Sven Magg, and Stefan Wermter. "Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks." International Journal of Computational Intelligence and Applications 17, no. 02 (June 2018): 1850008. http://dx.doi.org/10.1142/s1469026818500086.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Most learning algorithms require the practitioner to manually set the values of many hyperparameters before the learning process can begin. However, with modern algorithms, the evaluation of a given hyperparameter setting can take a considerable amount of time and the search space is often very high-dimensional. We suggest using a lower-dimensional representation of the original data to quickly identify promising areas in the hyperparameter space. This information can then be used to initialize the optimization algorithm for the original, higher-dimensional data. We compare this approach with the standard procedure of optimizing the hyperparameters only on the original input. We perform experiments with various state-of-the-art hyperparameter optimization algorithms such as random search, the tree of parzen estimators (TPEs), sequential model-based algorithm configuration (SMAC), and a genetic algorithm (GA). Our experiments indicate that it is possible to speed up the optimization process by using lower-dimensional data representations at the beginning, while increasing the dimensionality of the input later in the optimization process. This is independent of the underlying optimization procedure, making the approach promising for many existing hyperparameter optimization algorithms.

6

Yang, Zeshi, and Zhiqi Yin. "Efficient Hyperparameter Optimization for Physics-based Character Animation." Proceedings of the ACM on Computer Graphics and Interactive Techniques 4, no. 1 (April 26, 2021): 1–19. http://dx.doi.org/10.1145/3451254.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Physics-based character animation has seen significant advances in recent years with the adoption of Deep Reinforcement Learning (DRL). However, DRL-based learning methods are usually computationally expensive and their performance crucially depends on the choice of hyperparameters. Tuning hyperparameters for these methods often requires repetitive training of control policies, which is even more computationally prohibitive. In this work, we propose a novel Curriculum-based Multi-Fidelity Bayesian Optimization framework (CMFBO) for efficient hyperparameter optimization of DRL-based character control systems. Using curriculum-based task difficulty as fidelity criterion, our method improves searching efficiency by gradually pruning search space through evaluation on easier motor skill tasks. We evaluate our method on two physics-based character control tasks: character morphology optimization and hyperparameter tuning of DeepMimic. Our algorithm significantly outperforms state-of-the-art hyperparameter optimization methods applicable for physics-based character animation. In particular, we show that hyperparameters optimized through our algorithm result in at least 5x efficiency gain comparing to author-released settings in DeepMimic.

7

Han, Junjie, Cedric Gondro, and Juan Steibel. "98 Using differential evolution to improve predictive accuracy of deep learning models applied to pig production data." Journal of Animal Science 98, Supplement_3 (November 2, 2020): 27. http://dx.doi.org/10.1093/jas/skaa054.048.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Abstract Deep learning (DL) is being used for prediction in precision livestock farming and in genomic prediction. However, optimizing hyperparameters in DL models is critical for their predictive performance. Grid search is the traditional approach to select hyperparameters in DL, but it requires exhaustive search over the parameter space. We propose hyperparameter selection using differential evolution (DE), which is a heuristic algorithm that does not require exhaustive search. The goal of this study was to design and apply DE to optimize hyperparameters of DL models for genomic prediction and image analysis in pig production systems. One dataset consisted of 910 pigs genotyped with 28,916 SNP markers to predict their post-mortem meat pH. Another dataset consisted of 1,334 images of pigs eating inside a single-spaced feeder classified as: “single pig” or “multiple pigs.” The accuracy of genomic prediction was defined as the correlation between the predicted pH and the observed pH. The image classification prediction accuracy was the proportion of correctly classified images. For genomic prediction, a multilayer perceptron (MLP) was optimized. For image classification, MLP and convolutional neural networks (CNN) were optimized. For genomic prediction, the initial hyperparameter set resulted in an accuracy of 0.032 and for image classification, the initial accuracy was between 0.72 and 0.76. After optimization using DE, the genomic prediction accuracy was 0.3688 compared to 0.334 using GBLUP. The top selected models included one layer, 60 neurons, sigmoid activation and L2 penalty = 0.3. The accuracy of image classification after optimization was between 0.89 and 0.92. Selected models included three layers, adamax optimizer and relu or elu activation for the MLP, and one layer, 64 filters and 5×5 filter size for the CNN. DE can adapt the hyperparameter selection to each problem, dataset and model, and it significantly increased prediction accuracy with minimal user input.

8

Tsai, Chun-Wei, and Zhi-Yan Fang. "An Effective Hyperparameter Optimization Algorithm for DNN to Predict Passengers at a Metro Station." ACM Transactions on Internet Technology 21, no. 2 (March 30, 2021): 1–24. http://dx.doi.org/10.1145/3410156.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

As one of the public transportation systems, metro is certainly an indispensable part in urban areas of a metropolis today. Several successful results have shown that deep learning technologies might provide an effective way to predict the number of passengers at a metro station. However, most information systems based on deep learning technologies are usually designed and tuned manually by using domain knowledge and trial-and-error; thus, how to find out a set of suitable hyperparameters for a deep neural network (DNN) has become a critical research issue. To deal with the problem of hyperparameter setting for a DNN in solving the prediction of passengers at a metro station, a novel metaheuristic algorithm called search economics for hyperparameter optimization is presented to improve the accuracy rate of such a prediction system. The basic idea of the proposed algorithm is to divide the solution space into a set of subspaces first and then assign a different number of search agents to each subspace based on the “potential of each subspace.” The potential is estimated based on the objective values of the searched solutions, the objective values of the probe solutions, and the computation time invested in each subspace. The proposed method is compared with Bayesian, random forest, support vector regression, DNN, and DNN with different hyperparameter search algorithms, namely, grid search, simulated annealing, and particle swarm optimization. The simulation results using the data provided by the government of Taipei city, Taiwan, indicate that the proposed method outperforms all the other forecasting methods compared in this article in terms of the mean absolute percentage error.

9

Contreras, Pablo, Johanna Orellana-Alvear, Paul Muñoz, Jörg Bendix, and Rolando Célleri. "Influence of Random Forest Hyperparameterization on Short-Term Runoff Forecasting in an Andean Mountain Catchment." Atmosphere 12, no. 2 (February 10, 2021): 238. http://dx.doi.org/10.3390/atmos12020238.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

The Random Forest (RF) algorithm, a decision-tree-based technique, has become a promising approach for applications addressing runoff forecasting in remote areas. This machine learning approach can overcome the limitations of scarce spatio-temporal data and physical parameters needed for process-based hydrological models. However, the influence of RF hyperparameters is still uncertain and needs to be explored. Therefore, the aim of this study is to analyze the sensitivity of RF runoff forecasting models of varying lead time to the hyperparameters of the algorithm. For this, models were trained by using (a) default and (b) extensive hyperparameter combinations through a grid-search approach that allow reaching the optimal set. Model performances were assessed based on the R2, %Bias, and RMSE metrics. We found that: (i) The most influencing hyperparameter is the number of trees in the forest, however the combination of the depth of the tree and the number of features hyperparameters produced the highest variability-instability on the models. (ii) Hyperparameter optimization significantly improved model performance for higher lead times (12- and 24-h). For instance, the performance of the 12-h forecasting model under default RF hyperparameters improved to R2 = 0.41 after optimization (gain of 0.17). However, for short lead times (4-h) there was no significant model improvement (0.69 < R2 < 0.70). (iii) There is a range of values for each hyperparameter in which the performance of the model is not significantly affected but remains close to the optimal. Thus, a compromise between hyperparameter interactions (i.e., their values) can produce similar high model performances. Model improvements after optimization can be explained from a hydrological point of view, the generalization ability for lead times larger than the concentration time of the catchment tend to rely more on hyperparameterization than in what they can learn from the input data. This insight can help in the development of operational early warning systems.

10

Jervis, Michael, Mingliang Liu, and Robert Smith. "Deep learning network optimization and hyperparameter tuning for seismic lithofacies classification." Leading Edge 40, no. 7 (July 2021): 514–23. http://dx.doi.org/10.1190/tle40070514.1.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Deep learning is increasingly being applied in many aspects of seismic processing and interpretation. Here, we look at a deep convolutional neural network approach to multiclass seismic lithofacies characterization using well logs and seismic data. In particular, we focus on network performance and hyperparameter tuning. Several hyperparameter tuning approaches are compared, including true and directed random search methods such as very fast simulated annealing and Bayesian hyperparameter optimization. The results show that improvements in predictive capability are possible by using automatic optimization compared with manual parameter selection. In addition to evaluating the prediction accuracy's sensitivity to hyperparameters, we test various types of data representations. The choice of input seismic data can significantly impact the overall accuracy and computation speed of the optimized networks for the classification challenge under consideration. This is validated on a 3D synthetic seismic lithofacies example with acoustic and lithologic properties based on real well data and structure from an onshore oil field.

Більше джерел

Дисертації з теми "Hyperparameter search":

1

Wang, Jiexin. "Policy Hyperparameter Exploration for Behavioral Learning of Smartphone Robots." 京都大学 (Kyoto University), 2017. http://hdl.handle.net/2433/225744.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

2

Gabere, Musa Nur. "Prediction of antimicrobial peptides using hyperparameter optimized support vector machines." Thesis, University of the Western Cape, 2011. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_7345_1330684697.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Antimicrobial peptides (AMPs) play a key role in the innate immune response. They can be ubiquitously found in a wide range of eukaryotes including mammals, amphibians, insects, plants, and protozoa. In lower organisms, AMPs function merely as antibiotics by permeabilizing cell membranes and lysing invading microbes. Prediction of antimicrobial peptides is important because experimental methods used in characterizing AMPs are costly, time consuming and resource intensive and identification of AMPs in insects can serve as a template for the design of novel antibiotic. In order to fulfil this, firstly, data on antimicrobial peptides is extracted from UniProt, manually curated and stored into a centralized database called dragon antimicrobial peptide database (DAMPD). Secondly, based on the curated data, models to predict antimicrobial peptides are created using support vector machine with optimized hyperparameters. In particular, global optimization methods such as grid search, pattern search and derivative-free methods are utilised to optimize the SVM hyperparameters. These models are useful in characterizing unknown antimicrobial peptides. Finally, a webserver is created that will be used to predict antimicrobial peptides in haemotophagous insects such as Glossina morsitan and Anopheles gambiae.

3

Lundh, Felix, and Oscar Barta. "Hyperparameters relationship to the test accuracy of a convolutional neural network." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19846.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Machine learning for image classification is a hot topic and it is increasing in popularity. Therefore the aim of this study is to provide a better understanding of convolutional neural network hyperparameters by comparing the test accuracy of convolutional neural network models with different hyperparameter value configurations. The focus of this study is to see whether there is an influence in the learning process depending on which hyperparameter values were used. For conducting the experiments convolutional neural network models were developed using the programming language Python utilizing the library Keras. The dataset used for this study iscifar-10, it includes 60000 colour images of 10 categories ranging from man-made objects to different animal species. Grid search is used for instantiating models with varying learning rate and momentum, width and depth values. Learning rate is only tested combined with momentum and width is only tested combined with depth. Activation functions, convolutional layers and batch size are tested individually. Grid search is compared against Bayesian optimization to see which technique will find the most optimized learning rate and momentum values. Results illustrate that the impact different hyperparameters have on the overall test accuracy varies. Learning rate and momentum affects the test accuracy greatly, however suboptimal values for learning rate and momentum can decrease the test accuracy severely. Activation function, width and depth, convolutional layer and batch size have a lesser impact on test accuracy. Regarding Bayesian optimization compared to grid search, results show that Bayesian optimization will not necessarily find more optimal hyperparameter values.

4

Myrberger, Axel, and Essen Benjamin Von. "Classifying True and Fake Telecommunication Signals With Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297675.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

This project aimed to classified artificiality gener-ated,fake, and authentic,true, telecommunication signals, basedupon their frequency response, using methods from deep learn-ing. Another goal was to accomplish this with the least amountof dimension of data possible. The datasets used contained of anequal amount of measured, provided by Ericsson, and generated,by a WINNER II implementation in Matlab, frequency responses.It was determined that a normalized version of the absolute valueof the complex frequency response was enough information for afeedforward network to do a sufficient classification. To improvethe accuracy of the network we did a hyperparameter search,which allowed us to reach an accuracy of 90 percent on our testdataset. The results show that it is possible for neural networksto differentiate between true and fake telecommunication signalsbased on their frequency response, even if it is hard for a humanto tell the difference.
Målet med det här projektet var att klassificera artificiellt genererade signaler, falska, och riktiga, sanna, telekommunikation signaler med hjälp av signalernas frekvens- svar med djup inlärningsmetoder, deep learning. Ett annat mål med projektet var att klassificera signalerna med minsta möjliga antalet dimensioner av datan. Datasetet som användes bestod av till hälften av uppmät data som Ericsson har tillhandahållit, och till hälften av generad data ifrån en WINNER II modell implementerad i Matlab. En slutsats som kunde dras är att en normaliserad version av beloppet av det komplexa frekvenssvaret innehöll tillräckligt med information för att träna ett feedforward nätverk till att uppnå en hög klassificeringssäkerhet. För att vidare öka tillförlitligheten av nätverket gjordes en hyperparametersökning, detta ökade tillförligheten till 90 procent för testdataseten. Resultaten visar att det är möjligt för neurala nätverk att skilja mellan sanna och falska telekommunikations- signaler baserat på deras frekvenssvar, även om det är svårt för människor att skilja signalerna åt.
Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

5

Stynsberg, John. "Incorporating Scene Depth in Discriminative Correlation Filters for Visual Tracking." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153110.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Visual tracking is a computer vision problem where the task is to follow a targetthrough a video sequence. Tracking has many important real-world applications in several fields such as autonomous vehicles and robot-vision. Since visual tracking does not assume any prior knowledge about the target, it faces different challenges such occlusion, appearance change, background clutter and scale change. In this thesis we try to improve the capabilities of tracking frameworks using discriminative correlation filters by incorporating scene depth information. We utilize scene depth information on three main levels. First, we use raw depth information to segment the target from its surroundings enabling occlusion detection and scale estimation. Second, we investigate different visual features calculated from depth data to decide which features are good at encoding geometric information available solely in depth data. Third, we investigate handling missing data in the depth maps using a modified version of the normalized convolution framework. Finally, we introduce a novel approach for parameter search using genetic algorithms to find the best hyperparameters for our tracking framework. Experiments show that depth data can be used to estimate scale changes and handle occlusions. In addition, visual features calculated from depth are more representative if they were combined with color features. It is also shown that utilizing normalized convolution improves the overall performance in some cases. Lastly, the usage of genetic algorithms for hyperparameter search leads to accuracy gains as well as some insights on the performance of different components within the framework.

Частини книг з теми "Hyperparameter search":

1

Wistuba, Martin, Nicolas Schilling, and Lars Schmidt-Thieme. "Hyperparameter Search Space Pruning – A New Component for Sequential Model-Based Hyperparameter Optimization." In Machine Learning and Knowledge Discovery in Databases, 104–19. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23525-7_7.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

2

Plate, Tony. "Controlling the hyperparameter search in MacKay’s Bayesian neural network framework." In Lecture Notes in Computer Science, 93–112. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-49430-8_5.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

3

Plate, Tony. "Controlling the Hyperparameter Search in MacKay’s Bayesian Neural Network Framework." In Lecture Notes in Computer Science, 91–110. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35289-8_7.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

4

Ordozgoiti, Bruno, and Lluís A. Belanche Muñoz. "Off-the-Grid: Fast and Effective Hyperparameter Search for Kernel Clustering." In Machine Learning and Knowledge Discovery in Databases, 399–415. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67661-2_24.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

5

Florea, Adrian Cătălin, and Răzvan Andonie. "A Dynamic Early Stopping Criterion for Random Search in SVM Hyperparameter Optimization." In IFIP Advances in Information and Communication Technology, 168–80. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92007-8_15.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

6

Khamitov, Kamil, Nina Popova, Yuri Konkov, and Tony Castillo. "Tuning ANNs Hyperparameters and Neural Architecture Search Using HPC." In Communications in Computer and Information Science, 536–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64616-5_46.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

7

Jääsaari, Elias, Ville Hyvönen, and Teemu Roos. "Efficient Autotuning of Hyperparameters in Approximate Nearest Neighbor Search." In Advances in Knowledge Discovery and Data Mining, 590–602. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16145-3_46.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

8

Hinaut, Xavier, and Nathan Trouvain. "Which Hype for My New Task? Hints and Random Search for Echo State Networks Hyperparameters." In Lecture Notes in Computer Science, 83–97. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86383-8_7.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

9

Lopez, Kyra Mikaela M., and Ma Sheila A. Magboo. "A Clinical Decision Support Tool to Detect Invasive Ductal Carcinoma in Histopathological Images Using Support Vector Machines, Naïve-Bayes, and K-Nearest Neighbor Classifiers." In Machine Learning and Artificial Intelligence. IOS Press, 2020. http://dx.doi.org/10.3233/faia200765.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

This study aims to describe a model that will apply image processing and traditional machine learning techniques specifically Support Vector Machines, Naïve-Bayes, and k-Nearest Neighbors to identify whether or not a given breast histopathological image has Invasive Ductal Carcinoma (IDC). The dataset consisted of 54,811 breast cancer image patches of size 50px x 50px, consisting of 39,148 IDC negative and 15,663 IDC positive. Feature extraction was accomplished using Oriented FAST and Rotated BRIEF (ORB) descriptors. Feature scaling was performed using Min-Max Normalization while K-Means Clustering on the ORB descriptors was used to generate the visual codebook. Automatic hyperparameter tuning using Grid Search Cross Validation was implemented although it can also accept user supplied hyperparameter values for SVM, Naïve Bayes, and K-NN models should the user want to do experimentation. Aside from computing for accuracy, the AUPRC and MCC metrics were used to address the dataset imbalance. The results showed that SVM has the best overall performance, obtaining accuracy = 0.7490, AUPRC = 0.5536, and MCC = 0.2924.

10

Larsen, Kai R., and Daniel S. Becker. "Why Use Automated Machine Learning?" In Automated Machine Learning for Business, 1–22. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190941659.003.0001.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

Анотація:

Machine learning is involved in search, translation, detecting depression, likelihood of college dropout, finding lost children, and to sell all kinds of products. While barely beyond its inception, the current machine learning revolution will affect people and organizations no less than the Industrial Revolution’s effect on weavers and many other skilled laborers. Machine learning will automate hundreds of millions of jobs that were considered too complex for machines ever to take over even a decade ago, including driving, flying, painting, programming, and customer service, as well as many of the jobs previously reserved for humans in the fields of finance, marketing, operations, accounting, and human resources. This section explains how automated machine learning addresses exploratory data analysis, feature engineering, algorithm selection, hyperparameter tuning, and model diagnostics. The section covers the eight criteria considered essential for AutoML to have significant impact: accuracy, productivity, ease of use, understanding and learning, resource availability, process transparency, generalization, and recommended actions.

Тези доповідей конференцій з теми "Hyperparameter search":

1

Balaprakash, Prasanna, Michael Salim, Thomas D. Uram, Venkat Vishwanath, and Stefan M. Wild. "DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks." In 2018 IEEE 25th International Conference on High Performance Computing (HiPC). IEEE, 2018. http://dx.doi.org/10.1109/hipc.2018.00014.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

2

Lopez-Ramos, Luis M., and Baltasar Beferull-Lozano. "Online Hyperparameter Search Interleaved with Proximal Parameter Updates." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287537.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

3

Buratti, Benedetto J., and Eli Upfal. "Ordalia: Deep Learning Hyperparameter Search via Generalization Error Bounds Extrapolation." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006144.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

4

Cho, Minsu, and Chinmay Hegde. "Reducing the Search Space for Hyperparameter Optimization Using Group Sparsity." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682434.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

5

Li, Zhenzhen, Lianwen Jin, Chunlin Yang, and Zhuoyao Zhong. "Hyperparameter search for deep convolutional neural network using effect factors." In 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP). IEEE, 2015. http://dx.doi.org/10.1109/chinasip.2015.7230511.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

6

Sanchez, Odnan Ref, Matteo Repetto, Alessandro Carrega, and Raffaele Bolla. "Evaluating ML-based DDoS Detection with Grid Search Hyperparameter Optimization." In 2021 IEEE 7th International Conference on Network Softwarization (NetSoft). IEEE, 2021. http://dx.doi.org/10.1109/netsoft51509.2021.9492633.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

7

Zhang, Michael, Chandra Krintz, Markus Mock, and Rich Wolski. "Seneca: Fast and Low Cost Hyperparameter Search for Machine Learning Models." In 2019 IEEE 12th International Conference on Cloud Computing (CLOUD). IEEE, 2019. http://dx.doi.org/10.1109/cloud.2019.00071.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

8

Wendt, Alexander, Marco Wuschnig, and Martin Lechner. "Speeding up Common Hyperparameter Optimization Methods by a Two-Phase-Search." In IECON 2020 - 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2020. http://dx.doi.org/10.1109/iecon43393.2020.9254801.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

9

Shekar, B. H., and Guesh Dagnew. "Grid Search-Based Hyperparameter Tuning and Classification of Microarray Cancer Data." In 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP). IEEE, 2019. http://dx.doi.org/10.1109/icaccp.2019.8882943.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.

10

Alibrahim, Hussain, and Simone A. Ludwig. "Hyperparameter Optimization: Comparing Genetic Algorithm against Grid Search and Bayesian Optimization." In 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2021. http://dx.doi.org/10.1109/cec45853.2021.9504761.

Повний текст джерела

Стилі APA, Harvard, Vancouver, ISO та ін.