Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TLDR
Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.Abstract:
LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.read more
Citations
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Journal ArticleDOI
Communication-efficient algorithms for statistical optimization
TL;DR: A sharp analysis of this average mixture algorithm is provided, showing that under a reasonable set of conditions, the combined parameter achieves mean-squared error that decays as O(N-1 + (N/m)-2).
Journal ArticleDOI
FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning
Rukshan Batuwita,Vasile Palade +1 more
TL;DR: A method to improve FSVMs for CIL (called FSVM-CIL), which can be used to handle the class imbalance problem in the presence of outliers and noise.
Journal ArticleDOI
EmoNets: Multimodal deep learning approaches for emotion recognition in video
Samira Ebrahimi Kahou,Xavier Bouthillier,Pascal Lamblin,Caglar Gulcehre,Vincent Michalski,Kishore Konda,Sébastien Jean,Pierre Froumenty,Yann N. Dauphin,Nicolas Boulanger-Lewandowski,Raul Chandias Ferrari,Mehdi Mirza,David Warde-Farley,Aaron Courville,Pascal Vincent,Roland Memisevic,Chris Pal,Yoshua Bengio +17 more
TL;DR: In this article, the authors presented an approach to learn several specialist models using deep learning techniques, each focusing on one modality, including CNN, deep belief net, K-means based bag-of-mouths, and relational autoencoder.
Journal ArticleDOI
A review and analysis of regression and machine learning models on commercial building electricity load forecasting
TL;DR: In this article, a review of different electricity load forecasting models with a particular focus on regression models is presented, discussing different applications, most commonly used regression variables and methods to improve the performance and accuracy of the models.
Journal ArticleDOI
A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation
Meru A. Patil,Piyush Tagade,Krishnan S. Hariharan,Subramanya Mayya Kolake,Tae-won Song,Taejung Yeo,Seok-Gwang Doo +6 more
TL;DR: A novel method for real-time RUL estimation of Li ion batteries is proposed that integrates classification and regression attributes of Support Vector (SV) based machine learning technique.
References
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TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.
Statistical learning theory
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TL;DR: A simple procedure is proposed, which usually gives reasonable results and is suitable for beginners who are not familiar with SVM.
Journal ArticleDOI
A comparison of methods for multiclass support vector machines
Hsu Chih-Wei,Chih-Jen Lin +1 more
TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.