Today, signalprocessing research has a significantly widened its scope compared with just a few years ago [4], and machinelearning has been an important technical area of the signalprocessing society. Since 2006, d...
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Today, signalprocessing research has a significantly widened its scope compared with just a few years ago [4], and machinelearning has been an important technical area of the signalprocessing society. Since 2006, deep learning???a new area of machinelearning research???has emerged [7], impacting a wide range of signal and information processing work within the traditional and the new, widened scopes. Various workshops, such as the 2009 ICML workshop on learning Feature Hierarchies; the 2008 NIPS Deep learningworkshop: Foundations and Future Directions; and the 2009 NIPS workshop on Deep learning for Speech Recognition and Related Applications as well as an upcoming special issue on deep learning for speech and language processing in ieee Transactions on Audio, Speech, and Language processing (2010) have been devoted exclusively to deep learning and its applications to classical signalprocessing areas. We have also seen the government sponsor research on deep learning (e.g., the DARPA deep learning program, available at http://***/ipto/solicit/baa/BAA-09-40_***).
In this paper we present a new support vector machine (SVM) based classifier that is able to achieve better generalization as compared to the standard SVM. Better generalization is achieved by using a cascade of modif...
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ISBN:
(纸本)0780395174
In this paper we present a new support vector machine (SVM) based classifier that is able to achieve better generalization as compared to the standard SVM. Better generalization is achieved by using a cascade of modified proximal SVMs to remove simpler examples before presenting the difficult examples to a more complex SVM. The cascade structure uses the discrimination afforded by different feature spaces (by using different kernels) to simplify the classification task.
Clustering is a fundamental problem in machinelearning with numerous important applications in statistical signalprocessing, pattern recognition, and computer vision, where unsupervised analysis of data classificati...
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ISBN:
(纸本)0780395174
Clustering is a fundamental problem in machinelearning with numerous important applications in statistical signalprocessing, pattern recognition, and computer vision, where unsupervised analysis of data classification structures are required. The current state-of-the-art in clustering is widely accepted to be the so-called spectral clustering. Spectral clustering, based on pairwise affinities of samples imposes very large computational requirements. In this paper, we propose a vector quantization preprocessing stage for spectral clustering similar to the classical mean-shift principle for clustering. This preprocessing reduces the dimensionality of the matrix on which spectral techniques will be applied, resulting in significant computational savings.
This paper proposes development of Support Vector machines (SVMs) for detection and classification of rolling-element bearing faults. The training of the SVMs is carried out using the Sequential Minimal Optimization (...
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ISBN:
(纸本)0780395174
This paper proposes development of Support Vector machines (SVMs) for detection and classification of rolling-element bearing faults. The training of the SVMs is carried out using the Sequential Minimal Optimization (SMO) algorithm. In this paper, a mechanism for selecting adequate training parameters is proposed. This proposal makes the classification procedure fast and effective. Various scenarios are examined using two sets of vibration data, and the results are compared with those available in the literature that are relevant to this investigation.
In this paper we propose a new method for training classifiers for multi-class problems when classes are not (necessarily) mutually exclusive and may be related by means of a probabilistic tree structure. Our method i...
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ISBN:
(纸本)0780395174
In this paper we propose a new method for training classifiers for multi-class problems when classes are not (necessarily) mutually exclusive and may be related by means of a probabilistic tree structure. Our method is based on the definition of a Bayesian model relating network parameters, feature vectors and categories. learning is stated as a maximum likelihood estimation problem of the classifier parameters. The proposed algorithm is tested on an image retrieval scenario.
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