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Author: Haruhisa Takahashi

Affiliation: The University of Electro-Communications, Japan

ISBN: 978-989-758-018-5

Keyword(s): Autocorrelation Kernel, MRF, Mean-field, Fisher Score, Deep Learning, SVM.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Classification ; Computational Intelligence ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Kernel Methods ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Object Recognition ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Software Engineering ; Theory and Methods

Abstract: Deep learning methods, which include feature extraction in the training process, are achieving success in pattern recognition and machine learning fields but require huge parameter setting, and need the selection from various methods. On the contrary, Support Vector Machines (SVMs) have been popularly used in these fields in light of the simple algorithm and solid reasons based on the learning theory. However, it is difficult to improve recognition performance in SVMs beyond a certain level of capacity, in that higher dimensional feature space can only assure the linear separability of data as opposed to separation of the data manifolds themselves. We propose a new framework of kernel machine that generates essentially linearly separable kernel features. Our method utilizes pretraining process based on a kernel generative model and the mean field Fisher score with a higher-order autocorrelation kernel. Thus derived features are to be separated by a liner SVM, which exhibits far better generalization performance than any kernel-based SVMs. We show the experiments on the face detection using the appearance based approach, and that our method can attain comparable results with the state-of-the-art face detection methods based on AdaBoost, SURF, and cascade despite of smaller data size and no preprocessing. (More)

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Paper citation in several formats:
Takahashi H. (2014). Learning with Kernel Random Field and Linear SVM.In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 167-174. DOI: 10.5220/0004792601670174

@conference{icpram14,
author={Haruhisa Takahashi},
title={Learning with Kernel Random Field and Linear SVM},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={167-174},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004792601670174},
isbn={978-989-758-018-5},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Learning with Kernel Random Field and Linear SVM
SN - 978-989-758-018-5
AU - Takahashi H.
PY - 2014
SP - 167
EP - 174
DO - 10.5220/0004792601670174

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