Adaptive algorithms and stochastic approximations
Adaptive algorithms and stochastic approximations
Neural networks and the bias/variance dilemma
Neural Computation
A practical Bayesian framework for backpropagation networks
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
A Dynamical System Approach to Stochastic Approximations
SIAM Journal on Control and Optimization
Shape quantization and recognition with randomized trees
Neural Computation
A computational model for visual selection
Neural Computation
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Machine Learning
Rate of Convergence for Constrained Stochastic Approximation Algorithms
SIAM Journal on Control and Optimization
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
Feature subset selection bias for classification learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Iterative RELIEF for feature weighting
ICML '06 Proceedings of the 23rd international conference on Machine learning
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Feature selection based on the Shapley value
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Fast video retrieval under sparse training data
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
Feature selection with dynamic mutual information
Pattern Recognition
Review: A review of machine learning approaches to Spam filtering
Expert Systems with Applications: An International Journal
Multiclass classification and gene selection with a stochastic algorithm
Computational Statistics & Data Analysis
Feature selection for Bayesian network classifiers using the MDL-FS score
International Journal of Approximate Reasoning
GREC'09 Proceedings of the 8th international conference on Graphics recognition: achievements, challenges, and evolution
Time space tradeoffs in GA based feature selection for workload characterization
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Stochastic approximation for background modelling
Computer Vision and Image Understanding
Distributed stochastic approximation for constrained and unconstrained optimization
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
Stafflines pattern detection using the swarm intelligence algorithm
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Advances in Artificial Neural Systems
SOCIFS feature selection framework for handwritten authorship
International Journal of Hybrid Intelligent Systems
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We introduce a new model addressing feature selection from a large dictionary of variables that can be computed from a signal or an image. Features are extracted according to an efficiency criterion, on the basis of specified classification or recognition tasks. This is done by estimating a probability distribution P on the complete dictionary, which distributes its mass over the more efficient, or informative, components. We implement a stochastic gradient descent algorithm, using the probability as a state variable and optimizing a multi-task goodness of fit criterion for classifiers based on variable randomly chosen according to P. We then generate classifiers from the optimal distribution of weights learned on the training set. The method is first tested on several pattern recognition problems including face detection, handwritten digit recognition, spam classification and micro-array analysis. We then compare our approach with other step-wise algorithms like random forests or recursive feature elimination.