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» Choosing Multiple Parameters for Support Vector Machines
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CVPR
2001
IEEE
14 years 10 months ago
Component-based Face Detection
We present a component-based, trainable system for detecting frontal and near-frontal views of faces in still gray images. The system consists of a two-level hierarchy of Support ...
Bernd Heisele, Thomas Serre, Massimiliano Pontil, ...
NIPS
2004
13 years 9 months ago
Nonparametric Transforms of Graph Kernels for Semi-Supervised Learning
We present an algorithm based on convex optimization for constructing kernels for semi-supervised learning. The kernel matrices are derived from the spectral decomposition of grap...
Xiaojin Zhu, Jaz S. Kandola, Zoubin Ghahramani, Jo...
ICPR
2002
IEEE
14 years 9 months ago
Integrated Event Recognition from Multiple Sources
This paper proposes a system architecture for event recognition that integrates information from multiple sources (e.g., gesture and speech recognition from distributed sensors in...
Hiroaki Kawashima, Takashi Matsuyama
IJCAI
2007
13 years 9 months ago
Ensembles of Partially Trained SVMs with Multiplicative Updates
The training of support vector machines (SVM) involves a quadratic programming problem, which is often optimized by a complicated numerical solver. In this paper, we propose a muc...
Ivor W. Tsang, James T. Kwok
DMIN
2006
122views Data Mining» more  DMIN 2006»
13 years 9 months ago
Cost-Sensitive Analysis in Multiple Time Series Prediction
- In this paper we propose a new methodology for Cost-Benefit analysis in a multiple time series prediction problem. The proposed model is evaluated in a real world application bas...
Chamila Walgampaya, Mehmed M. Kantardzic