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» Incremental and Decremental Support Vector Machine Learning
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JMLR
2008
133views more  JMLR 2008»
14 years 9 months ago
Algorithms for Sparse Linear Classifiers in the Massive Data Setting
Classifiers favoring sparse solutions, such as support vector machines, relevance vector machines, LASSO-regression based classifiers, etc., provide competitive methods for classi...
Suhrid Balakrishnan, David Madigan
KDD
2006
ACM
165views Data Mining» more  KDD 2006»
15 years 10 months ago
Training linear SVMs in linear time
Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for highdimensional sparse data commonly encountered in applications like t...
Thorsten Joachims
JMLR
2010
185views more  JMLR 2010»
14 years 4 months ago
Multiple Kernel Learning on the Limit Order Book
Simple features constructed from order book data for the EURUSD currency pair were used to construct a set of kernels. These kernels were used both individually and simultaneously...
Tristan Fletcher, Zakria Hussain, John Shawe-Taylo...
MLDM
2005
Springer
15 years 3 months ago
Low-Level Cursive Word Representation Based on Geometric Decomposition
Abstract. An efficient low-level word image representation plays a crucial role in general cursive word recognition. This paper proposes a novel representation scheme, where a word...
Jian-xiong Dong, Adam Krzyzak, Ching Y. Suen, Domi...
ICDM
2003
IEEE
135views Data Mining» more  ICDM 2003»
15 years 3 months ago
An Algorithm for the Exact Computation of the Centroid of Higher Dimensional Polyhedra and its Application to Kernel Machines
The Support Vector Machine (SVM) solution corresponds to the centre of the largest sphere inscribed in version space. Alternative approaches like Bayesian Point Machines (BPM) and...
Frédéric Maire