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
Journal ArticleDOI
Identification of hybrid systems - A tutorial
TL;DR: In this paper, the identification of hybrid models with a hybrid structure is discussed, and the main features of the selected procedures are presented, and possible interactions to still enhance their effectiveness are suggested.
Journal ArticleDOI
Can SVM be used for automatic EEG detection of drowsiness during car driving
TL;DR: This study shows that automatic analysis and detection of EEG changes is possible by SVM and SVM is a good candidate for developing pre-emptive automatic drowsiness detection systems for driving safety.
Journal ArticleDOI
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Andreas Mayr,Günter Klambauer,Thomas Unterthiner,Marvin Steijaert,Jörg K. Wegner,Hugo Ceulemans,Djork-Arné Clevert,Sepp Hochreiter +7 more
TL;DR: The to date largest comparative study of nine state-of-the-art drug target prediction methods finds that deep learning outperforms all other competitors.
Book ChapterDOI
On the Use of the Adjusted Rand Index as a Metric for Evaluating Supervised Classification
TL;DR: This paper investigates the usability of this clustering validation measure in supervised classification problems by two different approaches: as a performance measure and in feature selection.
Journal ArticleDOI
A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series
TL;DR: The obtained empirical results show that the proposed forecasting model can capture the nonlinear properties of crude oil time series, and that better forecasting performance can be obtained in terms of precision and volatility than the other current forecasting models.
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.