Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TLDR
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.Abstract:
The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.
High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.read more
Citations
More filters
Proceedings ArticleDOI
Feature selection for support vector machines by means of genetic algorithm
TL;DR: This paper presents a special genetic algorithm, which especially takes into account the existing bounds on the generalization error for support vector machines (SVMs), which is compared to the traditional method of performing cross-validation and to other existing algorithms for feature selection.
Journal ArticleDOI
FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning
Rukshan Batuwita,Vasile Palade +1 more
TL;DR: A method to improve FSVMs for CIL (called FSVM-CIL), which can be used to handle the class imbalance problem in the presence of outliers and noise.
Book ChapterDOI
Applications of Support Vector Machines for Pattern Recognition: A Survey
Hyeran Byun,Seong-Whan Lee +1 more
TL;DR: A brief introduction of SVMs is described and its numerous applications are summarized, which show good generalization performance on many real-life data and the approach is properly motivated theoretically.
Journal ArticleDOI
Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying
TL;DR: In this paper, the authors present an approach for bullying detection based on state-of-the-art natural language processing and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life.
Journal ArticleDOI
Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes
TL;DR: Support vector machine modeling is a promising classification approach for detecting persons with common diseases such as diabetes and pre-diabetes in the population and should be further explored in other complex diseases using common variables.
References
More filters
Journal ArticleDOI
Learning representations by back-propagating errors
TL;DR: Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
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.
Book
Methods of Mathematical Physics
Richard Courant,David Hilbert +1 more
TL;DR: In this paper, the authors present an algebraic extension of LINEAR TRANSFORMATIONS and QUADRATIC FORMS, and apply it to EIGEN-VARIATIONS.