964 search results - page 134 / 193 » Incremental and Decremental Support Vector Machine Learning |
TKDE
2010
14 years 8 months ago
2010
—Local classifiers are sometimes called lazy learners because they do not train a classifier until presented with a test sample. However, such methods are generally not complet...
IDEAL
2004
Springer
15 years 3 months ago
2004
Springer
Abstract— The paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model gener...
JMLR
2006
14 years 9 months ago
2006
We propose an active set algorithm to solve the convex quadratic programming (QP) problem which is the core of the support vector machine (SVM) training. The underlying method is ...
ICML
2007
IEEE
15 years 10 months ago
2007
IEEE
We address feature selection problems for classification of small samples and high dimensionality. A practical example is microarray-based cancer classification problems, where sa...
ICML
2008
IEEE
15 years 10 months ago
2008
IEEE
The success of kernel methods including support vector machines (SVMs) strongly depends on the design of appropriate kernels. While initially kernels were designed in order to han...