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DATAMINE
1998
145views more  DATAMINE 1998»
15 years 6 months ago
A Tutorial on Support Vector Machines for Pattern Recognition
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-...
Christopher J. C. Burges
JMLR
2002
106views more  JMLR 2002»
15 years 6 months ago
Some Greedy Learning Algorithms for Sparse Regression and Classification with Mercer Kernels
We present some greedy learning algorithms for building sparse nonlinear regression and classification models from observational data using Mercer kernels. Our objective is to dev...
Prasanth B. Nair, Arindam Choudhury 0002, Andy J. ...
DATAMINE
1999
140views more  DATAMINE 1999»
15 years 6 months ago
A Scalable Parallel Algorithm for Self-Organizing Maps with Applications to Sparse Data Mining Problems
Abstract. We describe a scalable parallel implementation of the self organizing map (SOM) suitable for datamining applications involving clustering or segmentation against large da...
Richard D. Lawrence, George S. Almasi, Holly E. Ru...
SIGCSE
2002
ACM
114views Education» more  SIGCSE 2002»
15 years 6 months ago
Gender and information technology: implications of definitions
In this paper, we examine implications of definitions of information technology to women's participation in the industry and in academe. This paper is exploratory only, based...
Wendy L. Cukier, Denise Shortt, Irene Devine
TASLP
2002
84views more  TASLP 2002»
15 years 6 months ago
Maximum likelihood multiple subspace projections for hidden Markov models
The first stage in many pattern recognition tasks is to generate a good set of features from the observed data. Usually, only a single feature space is used. However, in some compl...
Mark J. F. Gales