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The following topics are dealt with: linear approximation; license plate recognition; color image segmentation; image quantization; wireless video transmission; congestion control; stochastic search; transmembrane helical segments; wavelet transform; semisupervised cluster algorithm; anomaly detection; data privacy; online market information processing; user behavior; particle swarm optimization;...
Statistical Learning Theory focuses on the machine learning theory for small samples. Support vector machine (SVM) are new methods based on statistical learning theory. There are many kinds of function can be used for kernel of SVM. Wavelet function is a set of bases that can approximate arbitrary functions in arbitrary precision. So Marr wavelet was used to construct wavelet kernel. On the other...
A predictive model of water-quality, which based on wavelet transform and support vector machine, is proposed. This model uses wavelet transform to get water time sequence variations in different scale, and optimizes three parameters of Regression Support Vector Machine with improved Particle Swarm Optimization algorithm, to improve the accuracy of prediction model. This model is used to take one-step...
The following topics are dealt with: compressed video indexing; particle swarm optimization; data clustering; image retrieval; video coding; augmented reality; video watermarking; medical image analysis; images fusion; SVM; image segmentation; feature selection; heterogeneous image databases; color texture classification; video surveillance; public transportation; pedestrian detection; Adaboost algorithm;...
Statistical Learning Theory focuses on the machine learning theory for small samples. Support vector machine (SVM) are new methods based on statistical learning theory. There are many kinds of function can be used for kernel of SVM. Wavelet function is a set of bases that can approximate arbitrary functions in arbitrary precision. So Marr wavelet was used to construct wavelet kernel. On the other...
To accurately predict the non-stationary time series, an approach based on integration of wavelet transform, PSO (Particle Swarm Optimization) and SVM (Support Vector Machine) is proposed. Wavelet decomposition is used to reduce the complexity of time series. Different components are predicted by their corresponding SVM forecasters, respectively, after wavelet transform. The final forecasting result...
Chaotic time series analysis or forecasting is an important and complex problem in machine learning. As an effective tool, support vector machine (SVM) has been broadly adopted in pattern recognition and machine learning fields. In developing a successful SVM classifier, eliminating noise and extracting feature are very important. This paper proposes the application of kernel PCA to LS-SVM for feature...
Based on least squares wavelet support vector machines (LS-WSVM) ensemble with quantum particle swarm optimization algorithm (QPSO), a systematic method for fault diagnosis of power circuits is presented. Firstly, wavelet coefficients of output voltage signals of power circuits under faulty conditions are obtained with wavelet lifting decomposition, and then faulty feature vectors are extracted from...
Based on quantum particle swarm optimization algorithm (QPSO), a novel approach of constructing multi-class least squares wavelet SVM (LS-WSVM) classifiers is presented, regularization parameters and kernel parameters of LS-WSVM can be optimized. Quantum particle swarm optimization can get appropriate parameters of LS-WSVM with global search, so the LS-WSVM model for the multi-class classifiers is...
Common used parameters selection method for support vector machines (SVM) is cross-validation, which is complicated calculation and takes a very long time. In this paper, a novel regularization parameter and kernel parameter tuning approach of SVM is presented based on quantum particle swarm optimization algorithm (QPSO). QPSO is a particle swarm optimization (PSO) with quantum individual that has...
A new method for load forecasting based on LS-SVM, PSO and wavelet transform is proposed. The wavelet transform is adopted to decompose the historical data, so the approximate part and several detail parts are obtained. The results of wavelet transform are predicted by a separate LS-SVM predictor. PSO is employed to determine these parameters of SVM model. The novel forecast model integrates the advantage...
In order to realize fast, precise and robust image registration, we proposed a new registration method, which is based on image feature extraction by wavelet transform combined with 2v-SVM and particle swarm optimization (PSO) algorithm. We made the affine transform between the source image and the target image, took Mutual Information (MI) as the Similarity Metric and extracted the feature points...
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