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» Large Margin Classification Using the Perceptron Algorithm
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SIGMOD
2004
ACM
150views Database» more  SIGMOD 2004»
16 years 2 months ago
When one Sample is not Enough: Improving Text Database Selection Using Shrinkage
Database selection is an important step when searching over large numbers of distributed text databases. The database selection task relies on statistical summaries of the databas...
Panagiotis G. Ipeirotis, Luis Gravano
BMCBI
2006
201views more  BMCBI 2006»
15 years 2 months ago
Gene selection algorithms for microarray data based on least squares support vector machine
Background: In discriminant analysis of microarray data, usually a small number of samples are expressed by a large number of genes. It is not only difficult but also unnecessary ...
E. Ke Tang, Ponnuthurai N. Suganthan, Xin Yao
NIPS
2004
15 years 3 months ago
Worst-Case Analysis of Selective Sampling for Linear-Threshold Algorithms
We provide a worst-case analysis of selective sampling algorithms for learning linear threshold functions. The algorithms considered in this paper are Perceptron-like algorithms, ...
Nicolò Cesa-Bianchi, Claudio Gentile, Luca ...
NIPS
2008
15 years 3 months ago
Scalable Algorithms for String Kernels with Inexact Matching
We present a new family of linear time algorithms based on sufficient statistics for string comparison with mismatches under the string kernels framework. Our algorithms improve t...
Pavel P. Kuksa, Pai-Hsi Huang, Vladimir Pavlovic
ICANN
2007
Springer
15 years 5 months ago
Resilient Approximation of Kernel Classifiers
Abstract. Trained support vector machines (SVMs) have a slow runtime classification speed if the classification problem is noisy and the sample data set is large. Approximating the...
Thorsten Suttorp, Christian Igel