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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The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data.
TL;DR: The Decoding Toolbox (TDT) is introduced which represents a user-friendly, powerful and flexible package for multivariate analysis of functional brain imaging data and offers a promising option for researchers who want to employ multivariate analyses of brain activity patterns.
Journal ArticleDOI
Pattern for python
Tom De Smedt,Walter Daelemans +1 more
TL;DR: Pattern is a package for Python 2.4+ with functionality for web mining, natural language processing, naturallanguage processing, machine learning, and network analysis for graph centrality and visualization.
Journal ArticleDOI
Predictive models for forecasting hourly urban water demand
TL;DR: This paper describes and compares a series of predictive models for forecasting water demand obtained using time series data from water consumption in an urban area of a city in south-eastern Spain, and proposes a simple model based on the weighted demand profile resulting from the exploratory analysis of the data.
Journal ArticleDOI
ECG Classification Using Wavelet Packet Entropy and Random Forests
Taiyong Li,Min Zhou +1 more
TL;DR: This paper proposes a method to classify ECG signals using wavelet packet entropy (WPE) and random forests (RF) following the Association for the Advancement of Medical Instrumentation (AAMI) recommendations and the inter-patient scheme, and shows that WPE and RF is promising for ECG classification.
Proceedings ArticleDOI
Inferring anchor links across multiple heterogeneous social networks
TL;DR: This paper proposes to extract heterogeneous features from multiple heterogeneous networks for anchor link prediction, including user's social, spatial, temporal and text information, and derives an effective solution, MNA (Multi-Network Anchoring), to infer anchor links w.r.t. the one-to-one constraint.
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.
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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.