Combining pattern recognition modalities at the sensor level via kernel fusion
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Principles of multi-kernel data mining
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
| Hi-index | 0.00 |
The new approach of relevant feature selection in machine learning is proposed for the case of ordered features. Feature selection and regularization of decision rule are combined in a single procedure. The selection of features is realized by introducing weight coefficients, characterizing degree of relevance of respective feature. A priori information about feature ordering is taken into account in the form of quadratic penalty or in the form of absolute value penalty on the difference of weight coefficients of neighboring features. Study of a penalty function in the form of absolute value shows computational complexity of such formulation. The effective method of solution is proposed. The brief survey of author's early papers, the mathematical frameworks, and experimental results are provided.