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
Machine Learning for Predictive Maintenance: A Multiple Classifier Approach
TL;DR: The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management, and can be used with high-dimensional and censored data problems, and the effectiveness of the methodology is demonstrated.
Proceedings ArticleDOI
YouTube2Text: Recognizing and Describing Arbitrary Activities Using Semantic Hierarchies and Zero-Shot Recognition
Sergio Guadarrama,Niveda Krishnamoorthy,Girish Malkarnenkar,Subhashini Venugopalan,Raymond J. Mooney,Trevor Darrell,Kate Saenko +6 more
TL;DR: This paper presents a solution that takes a short video clip and outputs a brief sentence that sums up the main activity in the video, such as the actor, the action and its object, and uses a Web-scale language model to ``fill in'' novel verbs.
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MaltParser: A Data-Driven Parser-Generator for Dependency Parsing
TL;DR: MaltParser is introduced, a data-driven parser generator for dependency parsing given a treebank in dependency format and can be used to induce a parser for the language of the treebank.
Journal ArticleDOI
Supervised and Traditional Term Weighting Methods for Automatic Text Categorization
TL;DR: This study investigates several widely-used unsupervised and supervised term weighting methods on benchmark data collections in combination with SVM and kNN algorithms and proposes a new simple supervisedterm weighting method, tf.rf, to improve the terms' discriminating power for text categorization task.
Journal ArticleDOI
Physical Human Activity Recognition Using Wearable Sensors.
Ferhat Attal,Samer Mohammed,Mariam Dedabrishvili,Faicel Chamroukhi,Latifa Oukhellou,Yacine Amirat +5 more
TL;DR: A review of different classification techniques used to recognize human activities from wearable inertial sensor data shows that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms.
References
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Journal ArticleDOI
Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
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
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Proceedings ArticleDOI
A training algorithm for optimal margin classifiers
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
A Practical Guide to Support Vector Classication
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.