Integer and combinatorial optimization
Integer and combinatorial optimization
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Bilinear separation of two sets in n-space
Computational Optimization and Applications
Machine Learning
Neural networks for pattern recognition
Neural networks for pattern recognition
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Feature Selection via Concave Minimization and Support Vector Machines
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Feature Selection Newton Method for Support Vector Machine Classification
Computational Optimization and Applications
Convex Optimization
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning structured prediction models: a large margin approach
ICML '05 Proceedings of the 22nd international conference on Machine learning
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Second Order Cone Programming Approaches for Handling Missing and Uncertain Data
The Journal of Machine Learning Research
Ensemble Pruning Via Semi-definite Programming
The Journal of Machine Learning Research
Linear Programs for Hypotheses Selection in Probabilistic Inference Models
The Journal of Machine Learning Research
Bayesian Network Learning with Parameter Constraints
The Journal of Machine Learning Research
Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming
The Journal of Machine Learning Research
Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problems
The Journal of Machine Learning Research
Maximum-Gain Working Set Selection for SVMs
The Journal of Machine Learning Research
Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
The Journal of Machine Learning Research
Building Support Vector Machines with Reduced Classifier Complexity
The Journal of Machine Learning Research
Exact 1-Norm Support Vector Machines Via Unconstrained Convex Differentiable Minimization
The Journal of Machine Learning Research
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Efficient Learning of Label Ranking by Soft Projections onto Polyhedra
The Journal of Machine Learning Research
Kernel-Based Learning of Hierarchical Multilabel Classification Models
The Journal of Machine Learning Research
Structured Prediction, Dual Extragradient and Bregman Projections
The Journal of Machine Learning Research
Linear Programming Relaxations and Belief Propagation -- An Empirical Study
The Journal of Machine Learning Research
Incremental Support Vector Learning: Analysis, Implementation and Applications
The Journal of Machine Learning Research
An Efficient Implementation of an Active Set Method for SVMs
The Journal of Machine Learning Research
Successive overrelaxation for support vector machines
IEEE Transactions on Neural Networks
Non-smoothness in classification problems
Optimization Methods & Software - THE JOINT EUROPT-OMS CONFERENCE ON OPTIMIZATION, 4-7 JULY, 2007, PRAGUE, CZECH REPUBLIC, PART I
Ranking hypotheses to minimize the search cost in probabilistic inference models
Discrete Applied Mathematics
Bias-Variance Analysis for Ensembling Regularized Multiple Criteria Linear Programming Models
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
A Study of Parts-Based Object Class Detection Using Complete Graphs
International Journal of Computer Vision
Creating and visualizing fuzzy document classification
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data
International Journal of Computer Vision
Review: Supervised classification and mathematical optimization
Computers and Operations Research
An expert system for automatically pruning vines
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
Hi-index | 0.00 |
The fields of machine learning and mathematical programming are increasingly intertwined. Optimization problems lie at the heart of most machine learning approaches. The Special Topic on Machine Learning and Large Scale Optimization examines this interplay. Machine learning researchers have embraced the advances in mathematical programming allowing new types of models to be pursued. The special topic includes models using quadratic, linear, second-order cone, semi-definite, and semi-infinite programs. We observe that the qualities of good optimization algorithms from the machine learning and optimization perspectives can be quite different. Mathematical programming puts a premium on accuracy, speed, and robustness. Since generalization is the bottom line in machine learning and training is normally done off-line, accuracy and small speed improvements are of little concern in machine learning. Machine learning prefers simpler algorithms that work in reasonable computational time for specific classes of problems. Reducing machine learning problems to well-explored mathematical programming classes with robust general purpose optimization codes allows machine learning researchers to rapidly develop new techniques. In turn, machine learning presents new challenges to mathematical programming. The special issue include papers from two primary themes: novel machine learning models and novel optimization approaches for existing models. Many papers blend both themes, making small changes in the underlying core mathematical program that enable the develop of effective new algorithms.