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» Learning with Rigorous Support Vector Machines
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AVSS
2006
IEEE
15 years 3 months ago
Classification-Based Likelihood Functions for Bayesian Tracking
The success of any Bayesian particle filtering based tracker relies heavily on the ability of the likelihood function to discriminate between the state that fits the image well an...
Chunhua Shen, Hongdong Li, Michael J. Brooks
ECAI
2006
Springer
15 years 3 months ago
Semantic Tree Kernels to Classify Predicate Argument Structures
Recent work on Semantic Role Labeling (SRL) has shown that syntactic information is critical to detect and extract predicate argument structures. As syntax is expressed by means of...
Alessandro Moschitti, Bonaventura Coppola, Daniele...
AVBPA
2003
Springer
133views Biometrics» more  AVBPA 2003»
15 years 3 months ago
LUT-Based Adaboost for Gender Classification
There are two main approaches to the problem of gender classification, Support Vector Machines (SVMs) and Adaboost learning methods, of which SVMs are better in correct rate but ar...
Bo Wu, Haizhou Ai, Chang Huang
AAAI
2008
15 years 2 months ago
Constrained Classification on Structured Data
Most standard learning algorithms, such as Logistic Regression (LR) and the Support Vector Machine (SVM), are designed to deal with i.i.d. (independent and identically distributed...
Chi-Hoon Lee, Matthew R. G. Brown, Russell Greiner...
ICPR
2010
IEEE
15 years 13 hour ago
Spatiotemporal-Boosted DCT Features for Head and Face Gesture Analysis
Automatic analysis of head gestures and facial expressions is a challenging research area and it has significant applications in humancomputer interfaces. In this study, facial la...
Hatice Çinar Akakin, Bülent Sankur