Add to ORKGInternational audienceIn this paper, a new learning method is proposed to build Support Vector Machines (SVMs) Binary Decision Functions (BDF) of reduced complexity and efficient generalization. The aim is to build a fast and efficient SVM classifier. A criterion is defined to evaluate the Decision Function Quality (DFQ) which blendes recognition rate and complexity of a BDF. Vector Quantization (VQ) is used to simplify the training set. A model selection based on the selection of the simplification level, of a feature subset and of SVM hyperparameters is performed to optimize the DFQ. Search space for selecting the best model being huge, Tabu Search (TS) is used to find a good sub-optimal model on tractable times. Experimental results show...
[Abstract]: Various machine learning methods have made a rapid transition to response modeling in se...
A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing...
We propose a classification method based on a decision tree whose nodes consist of linear Support Ve...
International audienceIn this paper, a new learning method is proposed to build Support Vector Machi...
International audienceA model selection method based on tabu search is proposed to build support vec...
Training SVM requires large memory and long cpu time when the pattern set is large. To alleviate the...
In this chapter, we revise several methods for SVM model selection, deriving from different approach...
Practical applications call for efficient model selection criteria for multiclass support vector mac...
When learning a support vector machine (SVM) from a set of labeled development patterns, the ultimat...
This approach aims to optimize the kernel parameters and to efficiently reduce the number of support...
Training SVM requires large memory and long cpu time when the pattern set is large. To alleviate the...
Support Vector Machine (SVM) has important properties such as a strong mathematical background and a...
In this communication we present a new algorithm for solving Support Vector Classifiers (SVC) with l...
The problem of determining optimal decision model is a difficult combinatorial task in the fields of...
This approach aims to optimize the kernel parameters and to efficiently reduce the number of support...
[Abstract]: Various machine learning methods have made a rapid transition to response modeling in se...
A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing...
We propose a classification method based on a decision tree whose nodes consist of linear Support Ve...
International audienceIn this paper, a new learning method is proposed to build Support Vector Machi...
International audienceA model selection method based on tabu search is proposed to build support vec...
Training SVM requires large memory and long cpu time when the pattern set is large. To alleviate the...
In this chapter, we revise several methods for SVM model selection, deriving from different approach...
Practical applications call for efficient model selection criteria for multiclass support vector mac...
When learning a support vector machine (SVM) from a set of labeled development patterns, the ultimat...
This approach aims to optimize the kernel parameters and to efficiently reduce the number of support...
Training SVM requires large memory and long cpu time when the pattern set is large. To alleviate the...
Support Vector Machine (SVM) has important properties such as a strong mathematical background and a...
In this communication we present a new algorithm for solving Support Vector Classifiers (SVC) with l...
The problem of determining optimal decision model is a difficult combinatorial task in the fields of...
This approach aims to optimize the kernel parameters and to efficiently reduce the number of support...
[Abstract]: Various machine learning methods have made a rapid transition to response modeling in se...
A common belief is that Machine Learning Theory (MLT) is not very useful, in pratice, for performing...
We propose a classification method based on a decision tree whose nodes consist of linear Support Ve...