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NECO
2008
112views more  NECO 2008»
15 years 6 months ago
Second-Order SMO Improves SVM Online and Active Learning
Iterative learning algorithms that approximate the solution of support vector machines (SVMs) have two potential advantages. First, they allow for online and active learning. Seco...
Tobias Glasmachers, Christian Igel
CDC
2008
IEEE
145views Control Systems» more  CDC 2008»
15 years 6 months ago
Necessary and sufficient conditions for success of the nuclear norm heuristic for rank minimization
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in control theory, machine learning, and discrete geometry. This c...
Benjamin Recht, Weiyu Xu, Babak Hassibi
SIAMCOMP
2010
172views more  SIAMCOMP 2010»
15 years 22 days ago
Deterministic Polynomial Time Algorithms for Matrix Completion Problems
We present new deterministic algorithms for several cases of the maximum rank matrix completion problem (for short matrix completion), i.e. the problem of assigning values to the ...
Gábor Ivanyos, Marek Karpinski, Nitin Saxen...
AVBPA
2003
Springer
133views Biometrics» more  AVBPA 2003»
15 years 9 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
ICIP
2006
IEEE
16 years 7 months ago
An Efficient Method for the Removal of Impulse Noise
A computationally efficient algorithm is proposed to remove noise impulses from speech and audio signals while retaining its features and tonal quality. The proposed method is bas...
Wenbin Luo, Dung Dang