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» Choosing Multiple Parameters for Support Vector Machines
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CVPR
2001
IEEE
15 years 12 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
14 years 11 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
15 years 11 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
14 years 11 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»
14 years 11 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