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CIVR
2006
Springer
121views Image Analysis» more  CIVR 2006»
15 years 1 months ago
Finding Faces in Gray Scale Images Using Locally Linear Embeddings
The problem of face detection remains challenging because faces are non-rigid objects that have a high degree of variability with respect to head rotation, illumination, facial exp...
Samuel Kadoury, Martin D. Levine
AAAI
2008
15 years 9 days 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...
JCP
2008
167views more  JCP 2008»
14 years 10 months ago
Accelerated Kernel CCA plus SVDD: A Three-stage Process for Improving Face Recognition
kernel canonical correlation analysis (KCCA) is a recently addressed supervised machine learning methods, which shows to be a powerful approach of extracting nonlinear features for...
Ming Li, Yuanhong Hao
CLASSIFICATION
2004
59views more  CLASSIFICATION 2004»
14 years 9 months ago
Oscillation Heuristics for the Two-group Classification Problem
: We propose a new nonparametric family of oscillation heuristics for improving linear classifiers in the two-group discriminant problem. The heuristics are motivated by the intuit...
Ognian Asparouhov, Paul A. Rubin
TKDE
2010
168views more  TKDE 2010»
14 years 8 months ago
Completely Lazy Learning
—Local classifiers are sometimes called lazy learners because they do not train a classifier until presented with a test sample. However, such methods are generally not complet...
Eric K. Garcia, Sergey Feldman, Maya R. Gupta, San...