An Introduction to Kernel Learning Algorithms

2009 ◽  
pp. 25-48 ◽  
Author(s):  
Peter V. Gehler ◽  
Bernhard Schlkopf
Keyword(s):  
Learning Algorithms ◽  
Kernel Learning

scholarly journals Nonlocal Estimation of Manifold Structure

2006 ◽  
Vol 18 (10) ◽  
pp. 2509-2528 ◽  
Author(s):  
Yoshua Bengio ◽  
Martin Monperrus ◽  
Hugo Larochelle
Keyword(s):  
Large Class ◽  
Manifold Learning ◽  
Learning Algorithms ◽  
Tangent Plane ◽  
Training Data ◽  
Kernel Learning ◽  
Manifold Structure ◽  
Prediction Function ◽  
Underlying Manifold ◽  
Shared Structure

We claim and present arguments to the effect that a large class of manifold learning algorithms that are essentially local and can be framed as kernel learning algorithms will suffer from the curse of dimensionality, at the dimension of the true underlying manifold. This observation invites an exploration of nonlocal manifold learning algorithms that attempt to discover shared structure in the tangent planes at different positions. A training criterion for such an algorithm is proposed, and experiments estimating a tangent plane prediction function are presented, showing its advantages with respect to local manifold learning algorithms: it is able to generalize very far from training data (on learning handwritten character image rotations), where local nonparametric methods fail.

Phoneme recognition with kernel learning algorithms

2004 ◽  
Vol 116 (4) ◽  
pp. 2480-2480
Author(s):  
Hassan H. Namarvar ◽  
Theodore W. Berger
Keyword(s):  
Learning Algorithms ◽  
Kernel Learning ◽  
Phoneme Recognition

ESTIMATION OF MENTAL FATIGUE BASED ON WAVELET PACKET PARAMETERS AND KERNEL LEARNING ALGORITHMS

2008 ◽  
Vol 06 (05) ◽  
pp. 719-737 ◽  
Author(s):  
CHONG ZHANG ◽  
CHONG-XUN ZHENG ◽  
MING-PU ZHAO ◽  
XIAO-LIN YU
Keyword(s):  
Performance Test ◽  
Mental Arithmetic ◽  
Wavelet Packet ◽  
Learning Algorithms ◽  
Mental Fatigue ◽  
Kernel Learning ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Arithmetic Task ◽  
The Impact

A new method by combining wavelet packet transform with kernel learning algorithms is proposed to estimate the mental fatigue state in this paper. The first step of this method is to investigate the impact of long term mental arithmetic task on psychology and physiology of subjects by subjective self-reporting measures, action performance test, power spectral indices of HRV and wavelet packet parameters of EEG. The second step is to calculate the wavelet packet features of all EEG data segments, including relative wavelet packet energy parameters in four frequency bands, wavelet packet entropy and three ratio indices. Finally, kernel principal component analysis (KPCA) and support vector machine (SVM) are jointly applied to differentiate two mental fatigue states. The investigation suggests that the joint KPCA-SVM method is able to effectively reduce the dimensionality of the feature vectors, speed up the convergence in the training of SVM and achieve higher classification accuracy (88%) of the mental fatigue state. Hence KPCA-SVM could be a promising model for the estimation of mental fatigue.

Evaluation of mental fatigue based on multipsychophysiological parameters and kernel learning algorithms

2008 ◽  
Vol 53 (12) ◽  
pp. 1835-1847 ◽  
Author(s):  
Chong Zhang ◽  
ChongXun Zheng ◽  
XiaoLin Yu
Keyword(s):  
Learning Algorithms ◽  
Mental Fatigue ◽  
Kernel Learning
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