Instance-Based Learning Algorithms
Machine Learning
Advances in neural information processing systems 2
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Unifying instance-based and rule-based induction
Machine Learning
An equivalence between sparse approximation and support vector machines
Neural Computation
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Optimal control by least squares support vector machines
Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Ridge Regression Learning Algorithm in Dual Variables
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Training multilayer perceptron classifiers based on a modified support vector method
IEEE Transactions on Neural Networks
The evidence framework applied to support vector machines
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Adapting two-class support vector classification methods to many class problems
ICML '05 Proceedings of the 22nd international conference on Machine learning
Endoscopy images classification with Kernel based learning algorithms
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Convergence of the IRWLS Procedure to the Support Vector Machine Solution
Neural Computation
A Fast Feature-based Dimension Reduction Algorithm for Kernel Classifiers
Neural Processing Letters
The theoretical analysis of FDA and applications
Pattern Recognition
A process model to develop an internal rating system: sovereign credit ratings
Decision Support Systems
K-T.R.A.C.E: A kernel k-means procedure for classification
Computers and Operations Research
Text classification: A least square support vector machine approach
Applied Soft Computing
Hyperplane algorithm - first step of the paired planes classification procedure
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Mining software repositories for comprehensible software fault prediction models
Journal of Systems and Software
An improved Naive Bayesian classifier with advanced discretisation method
International Journal of Intelligent Systems Technologies and Applications
Predicting going concern opinion with data mining
Decision Support Systems
Recursive reduced least squares support vector regression
Pattern Recognition
An efficient algorithm for learning to rank from preference graphs
Machine Learning
Domain adaptation from multiple sources via auxiliary classifiers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Least squares one-class support vector machine
Pattern Recognition Letters
Model selection for the LS-SVM. Application to handwriting recognition
Pattern Recognition
A framework for kernel-based multi-category classification
Journal of Artificial Intelligence Research
IP-LSSVM: A two-step sparse classifier
Pattern Recognition Letters
Exploiting scale-free information from expression data for cancer classification
Computational Biology and Chemistry
Conditional Density Estimation with HMM Based Support Vector Machines
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Help-training semi-supervised LS-SVM
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Expert Systems with Applications: An International Journal
A study of Taiwan's issuer credit rating systems using support vector machines
Expert Systems with Applications: An International Journal
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Group search optimizer: an optimization algorithm inspired by animal searching behavior
IEEE Transactions on Evolutionary Computation
Prune support vector machines by an iterative process
International Journal of Computers and Applications
Optimized fixed-size kernel models for large data sets
Computational Statistics & Data Analysis
Expert Systems with Applications: An International Journal
Least-Squares Support Vector Machine Approach to Viral Replication Origin Prediction
INFORMS Journal on Computing
Computers in Biology and Medicine
Soft Nearest Convex Hull Classifier
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Identifying financially successful start-up profiles with data mining
Expert Systems with Applications: An International Journal
First and Second Order SMO Algorithms for LS-SVM Classifiers
Neural Processing Letters
Evolution strategies based adaptive Lp LS-SVM
Information Sciences: an International Journal
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
Help-Training for semi-supervised support vector machines
Pattern Recognition
Tuning metaheuristics: A data mining based approach for particle swarm optimization
Expert Systems with Applications: An International Journal
Journal of Signal Processing Systems
Detecting bots via incremental LS-SVM learning with dynamic feature adaptation
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Discriminative experimental design
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Improved conjugate gradient implementation for least squares support vector machines
Pattern Recognition Letters
PSO-Based hyper-parameters selection for LS-SVM classifiers
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Classification with support hyperplanes
ECML'06 Proceedings of the 17th European conference on Machine Learning
Improved modeling of clinical data with kernel methods
Artificial Intelligence in Medicine
An adaptive support vector machine learning algorithm for large classification problem
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A group search optimizer for neural network training
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
Performance of classification models from a user perspective
Decision Support Systems
Review: Supervised classification and mathematical optimization
Computers and Operations Research
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
A computational geometry approach for pareto-optimal selection of neural networks
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Artificial neural network training using a new efficient optimization algorithm
Applied Soft Computing
Who do you call? problem resolution through social compute units
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
Learning attribute relation in attribute-based zero-shot classification
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Multi-output least-squares support vector regression machines
Pattern Recognition Letters
3D modeling of multiple-object scenes from sets of images
Pattern Recognition
Efficient sparse least squares support vector machines for pattern classification
Computers & Mathematics with Applications
Fast sparse approximation of extreme learning machine
Neurocomputing
Hi-index | 0.01 |
In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of equations in the dual space. While the SVM classifier has a large margin interpretation, the LS-SVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization problems are represented by a set of binary classifiers using different output coding schemes. While regularization is used to control the effective number of parameters of the LS-SVM classifier, the sparseness property of SVMs is lost due to the choice of the 2-norm. Sparseness can be imposed in a second stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function (RBF) kernels. Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LS-SVM performances are consistently very good when compared to a variety of methods described in the literature including decision tree based algorithms, statistical algorithms and instance based learning methods. We show on ten UCI datasets that the LS-SVM sparse approximation procedure can be successfully applied.