Journal ArticleDOI
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
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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
iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC.
Zhen-Dong Su,Yan Huang,Zhao-Yue Zhang,Ya-Wei Zhao,Dong Wang,Dong Wang,Wei Chen,Wei Chen,Kuo-Chen Chou,Hao Lin +9 more
TL;DR: A sequence‐based bioinformatics tool called ‘iLoc‐lncRNA’ is developed to predict the subcellular locations of LncRNAs by incorporating the 8‐tuple nucleotide features into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach.
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Turning from TF-IDF to TF-IGM for term weighting in text classification
TL;DR: Experimental results show that TF-IGM outperforms the famous TF-IDF and the state-of-the-art supervised term weighting schemes and some new findings different from previous studies are obtained and analyzed in depth in the paper.
Journal ArticleDOI
Online binary decision decoding using functional near-infrared spectroscopy for the development of brain–computer interface
TL;DR: It is observed and verified, using the linear discriminant analysis (LDA) and the support vector machine (SVM) classifications, that the cortical hemodynamic responses for making a “yes” decision are distinguishable from those forMaking a ‘no’ decision.
Journal ArticleDOI
A Pattern-Based Approach for Sarcasm Detection on Twitter
Mondher Bouazizi,Tomoaki Ohtsuki +1 more
TL;DR: This paper proposes a pattern-based approach to detect sarcasm on Twitter and proposes four sets of features that cover the different types of sarcasm, which are used to classify tweets as sarcastic and non-sarcastic.
Proceedings ArticleDOI
Unfolding physiological state: mortality modelling in intensive care units
Marzyeh Ghassemi,Tristan Naumann,Finale Doshi-Velez,Nicole J. Brimmer,Rohit Joshi,Anna Rumshisky,Peter Szolovits +6 more
TL;DR: This work examined the use of latent variable models to decompose free-text hospital notes into meaningful features, and found that latent topic-derived features were effective in determining patient mortality under three timelines: in-hospital, 30 day post- Discharge, and 1 year post-discharge mortality.
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.