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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Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via Ames test.
TL;DR: The results demonstrate that the described computation methods are useful to identify potential anticancer peptides, which are worthy of further experimental validation and 2 peptides of HIV-1 p24 protein can be used as new anticancer candidates without mutagenicity.
Proceedings ArticleDOI
An efficient algorithm for a class of fused lasso problems
Jun Liu,Lei Yuan,Jieping Ye +2 more
TL;DR: This paper proposes an Efficient Fused Lasso Algorithm (EFLA) and designs a restart technique to accelerate the convergence of SFA, by exploiting the special "structures" of both the original and the reformulated FLSA problems.
Journal ArticleDOI
Estimation of immune cell content in tumour tissue using single-cell RNA-seq data
Max Schelker,Sonia Feau,Jinyan Du,Nav Ranu,Edda Klipp,Gavin MacBeath,Birgit Schoeberl,Andreas Raue +7 more
TL;DR: The CYBERSORT-based deconvolution algorithm is optimised by including cell type-specific reference gene expression profiles generated from tumour-derived single-cell RNA sequencing data, which can estimate the content of many tumours associated immune and stromal cell types, their therapeutically relevant ratios, as well as an improved gene expression profile of the malignant cells.
Journal ArticleDOI
2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function
TL;DR: By incorporating the physicochemical properties of nucleotides into the pseudo K-tuple nucleotide composition (PseKNC), a powerful predictor called 2L-piRNA is proposed, a two-layer ensemble classifier in which the first layer is for identifying whether a query RNA molecule is piRNA or non-pi RNA, and the second layer for identifyingWhether a piRNA is with or without the function of instructing target mRNA deadenylation.
Journal ArticleDOI
Assessment of the effectiveness of support vector machines for hyperspectral data
Mahesh Pal,Paul M. Mather +1 more
TL;DR: Results show that the SVM performs better than maximum likelihood, univariate decision tree and backpropagation neural network classifiers, even with small training data sets, and is almost unaffected by the Hughes phenomenon.
References
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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.
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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.