A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Training Invariant Support Vector Machines
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Predicting distribution of a new forest disease using one-class SVMs
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Using latent semantic indexing to filter spam
Proceedings of the 2003 ACM symposium on Applied computing
Learning to crawl: Comparing classification schemes
ACM Transactions on Information Systems (TOIS)
Journal of Biomedical Informatics
EFFECTIVENESS OF SUPPORT VECTOR MACHINE FOR CRIME HOT-SPOTS PREDICTION
Applied Artificial Intelligence
Structural and Multidisciplinary Optimization
Support vector machine: A tool for mapping mineral prospectivity
Computers & Geosciences
A sparse Gaussian process regression model for tourism demand forecasting in Hong Kong
Expert Systems with Applications: An International Journal
Genetic optimized artificial immune system in spam detection: a review and a model
Artificial Intelligence Review
Hi-index | 0.00 |
Kernel methods, a new generation of learning algorithms, utilize techniques from optimization, statistics, and functional analysis to achieve maximal generality, flexibility, and performance. These algorithms are different from earlier techniques used in machine learning in many respects: For example, they are explicitly based on a theoretical model of learning rather than on loose analogies with natural learning systems or other heuristics. They come with theoretical guarantees about their performance and have a modular design that makes it possible to separately implement and analyze their components. They are not affected by the problem of local minima because their training amounts to convex optimization. In the last decade, a sizable community of theoreticians and practitioners has formed around these methods, and a number of practical applications have been realized. Although the research is not concluded, already now kernel methods are considered the state of the art in several machine learning tasks. Their ease of use, theoretical appeal, and remarkable performance have made them the system of choice for many learning problems. Successful applications range from text categorization to handwriting recognition to classification of gene-expression data.