A novel LS-SVMs hyper-parameter selection based on particle swarm optimization

[1]  Chengyi Xiong,et al.  Novel algorithm for image interpolation , 2004 .

[2]  Gunnar Rätsch,et al.  Soft Margins for AdaBoost , 2001, Machine Learning.

[3]  Bernhard Schölkopf,et al.  Feature selection for support vector machines by means of genetic algorithm , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.

[4]  Helge J. Ritter,et al.  Discriminative Densities from Maximum Contrast Estimation , 2002, NIPS.

[5]  V. Vapnik,et al.  Bounds on Error Expectation for Support Vector Machines , 2000, Neural Computation.

[6]  Yanchun Liang,et al.  A PARTICLE SWARM OPTIMIZATION-BASED ALGORITHM FOR JOB-SHOP SCHEDULING PROBLEMS , 2005 .

[7]  Johan A. K. Suykens,et al.  Sparse approximation using least squares support vector machines , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

[8]  Christian Igel,et al.  Gradient-Based Adaptation of General Gaussian Kernels , 2005, Neural Computation.

[9]  Kemal Polat,et al.  Detection of ECG Arrhythmia using a differential expert system approach based on principal component analysis and least square support vector machine , 2007, Appl. Math. Comput..

[10]  Ching Y. Suen,et al.  Automatic model selection for the optimization of SVM kernels , 2005, Pattern Recognit..

[11]  Johan A. K. Suykens,et al.  Benchmarking Least Squares Support Vector Machine Classifiers , 2004, Machine Learning.

[12]  Nurettin Acir.  Classification of ECG beats by using a fast least square support vector machines with a dynamic programming feature selection algorithm , 2005, Neural Computing & Applications.

[13]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[14]  J P Doucet,et al.  QSAR and classification study of 1,4-dihydropyridine calcium channel antagonists based on least squares support vector machines. , 2005, Molecular pharmaceutics.

[15]  Simon J. Perkins,et al.  Genetic Algorithms and Support Vector Machines for Time Series Classification , 2002, Optics + Photonics.

[16]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[17]  Dong Xiang,et al.  The Bias-Variance Tradeoff and the Randomized GACV , 1998, NIPS.

[18]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[19]  Kok Seng Chua,et al.  Efficient computations for large least square support vector machine classifiers , 2003, Pattern Recognit. Lett..

[20]  Sayan Mukherjee,et al.  Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.

[21]  F. Tan,et al.  Prediction of mitochondrial proteins based on genetic algorithm – partial least squares and support vector machine , 2007, Amino Acids.

[22]  Zhide Hu,et al.  Quantitative structure–activity relationship study of acyl ureas as inhibitors of human liver glycogen phosphorylase using least squares support vector machines , 2007 .

[23]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[24]  Kemal Polat,et al.  A novel approach to estimation of E. coli promoter gene sequences: Combining feature selection and least square support vector machine (FS_LSSVM) , 2007, Appl. Math. Comput..

[25]  Farid Melgani,et al.  Semisupervised PSO-SVM Regression for Biophysical Parameter Estimation , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[27]  Fei Liu,et al.  Use of visible and near infrared spectroscopy and least squares-support vector machine to determine soluble solids content and pH of cola beverage. , 2007, Journal of agricultural and food chemistry.

[28]  S. Sathiya Keerthi,et al.  Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms , 2002, IEEE Trans. Neural Networks.

[29]  Chih-Jen Lin,et al.  Radius Margin Bounds for Support Vector Machines with the RBF Kernel , 2002, Neural Computation.

[30]  Carl Gold,et al.  Model selection for support vector machine classification , 2002, Neurocomputing.

[31]  Elena Marchiori,et al.  Analysis of Proteomic Pattern Data for Cancer Detection , 2004, EvoWorkshops.

[32]  Christian Igel,et al.  Evolutionary tuning of multiple SVM parameters , 2005, ESANN.

[33]  Ronald M. Summers,et al.  Feature selection for computer-aided polyp detection using genetic algorithms , 2003, SPIE Medical Imaging.

[34]  Satarupa Banerjee,et al.  Text classification: A least square support vector machine approach , 2007, Appl. Soft Comput..

[35]  S. Günes,et al.  Pattern Detection of Atherosclerosis from Carotid Artery Doppler Signals using Fuzzy Weighted Pre-Processing and Least Square Support Vector Machine (LSSVM) , 2007, Annals of Biomedical Engineering.

[36]  Olivier Chapelle,et al.  Model Selection for Support Vector Machines , 1999, NIPS.

[37]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

[38]  Ping-Feng Pai,et al.  Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting , 2005 .

[39]  Senjian An,et al.  Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression , 2007, Pattern Recognit..

[40]  James C. Bezdek,et al.  Applications and Science of Neural Networks, Fuzzy Systems and Evolutionary Computation IV , 1998 .

[41]  Ming-Wei Chang,et al.  Leave-One-Out Bounds for Support Vector Regression Model Selection , 2005, Neural Computation.

[42]  Jian-Hui Jiang,et al.  Radial Basis Function Network-Based Transform for a Nonlinear Support Vector Machine as Optimized by a Particle Swarm Optimization Algorithm with Application to QSAR Studies , 2007, J. Chem. Inf. Model..

[43]  Shao-hui Tao,et al.  SVD-LSSVM and its application in chemical pattern classification , 2006 .