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
More filters
Journal ArticleDOI
Activity Recognition Using a Single Accelerometer Placed at the Wrist or Ankle
TL;DR: A classification algorithm using 13 features shows good classification into the four classes given the complexity of the activities in the original data set, and could be implemented in real time on mobile devices with only 4-s latency.
Journal ArticleDOI
A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines
TL;DR: Two important improvements to the SVR based load forecasting method are introduced, i.e., procedure for generation of model inputs and subsequent model input selection using feature selection algorithms and the use of the particle swarm global optimization based technique for the optimization of SVR hyper-parameters reduces the operator interaction.
Journal ArticleDOI
Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer
Zhenyu Liu,Xiao-Yan Zhang,Yan-Jie Shi,Lin Wang,Hai-Tao Zhu,Zhenchao Tang,Shuo Wang,Xiao-Ting Li,Jie Tian,Ying-Shi Sun +9 more
TL;DR: Using pre- and posttreatment MRI data, a radiomics model with excellent performance for individualized, noninvasive prediction of pCR is developed and may be used to identify LARC patients who can omit surgery after chemoradiotherapy.
Journal ArticleDOI
Using Hashtags to Capture Fine Emotion Categories from Tweets
TL;DR: It is shown that emotion‐word hashtags are good manual labels of emotions in tweets and a method to generate a large lexicon of word–emotion associations from this emotion‐labeled tweet corpus is proposed, which is the first lexicon with real‐valued word‐emotion association scores.
Journal ArticleDOI
Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach
TL;DR: A new approach that exploits multi-seasonal information in dense time stacks of Landsat imagery using a multi-date composite change detection technique that proved particularly effective for monitoring peri-urbanization outside the urban core, capturing > 98% of village settlements.
References
More filters
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.