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» A Hierarchical Classifier Using New Support Vector Machine
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NIPS
2000
13 years 9 months ago
The Kernel Trick for Distances
A method is described which, like the kernel trick in support vector machines (SVMs), lets us generalize distance-based algorithms to operate in feature spaces, usually nonlinearl...
Bernhard Schölkopf
JMLR
2010
121views more  JMLR 2010»
13 years 2 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Shiliang Sun, John Shawe-Taylor
BMCBI
2005
120views more  BMCBI 2005»
13 years 7 months ago
pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties
Background: Protein subcellular localization is an important determinant of protein function and hence, reliable methods for prediction of localization are needed. A number of pre...
Deepak Sarda, Gek Huey Chua, Kuo-Bin Li, Arun Kris...
ICPR
2010
IEEE
13 years 10 months ago
Fast Training of Object Detection Using Stochastic Gradient Descent
Training datasets for object detection problems are typically very large and Support Vector Machine (SVM) implementations are computationally complex. As opposed to these complex ...
Rob Wijnhoven, Peter H. N. De With
KDD
2005
ACM
118views Data Mining» more  KDD 2005»
14 years 8 months ago
On the use of linear programming for unsupervised text classification
We propose a new algorithm for dimensionality reduction and unsupervised text classification. We use mixture models as underlying process of generating corpus and utilize a novel,...
Mark Sandler