An evolutionary approach to constraint-regularized learning

The success of machine learning methods for inducing models from data crucially depends on the proper incorporation of background knowledge about the model to be learned. The idea of constraint-regularized learning is to em- ploy fuzzy set-based modeling techniques in order to express such knowl- edge in a flexible way, and to formalize it in terms of fuzzy constraints. Thus, background knowledge can be used to appropriately bias the learn- ing process within the regularization framework of inductive inference. After a brief review of this idea, the paper offers an operationalization of constraint- regularized learning. The corresponding framework is based on evolutionary methods for model optimization and employs fuzzy rule bases of the Takagi- Sugeno type as flexible function approximators

Microscopic theory of nuclear reactions for a multi-particle-break-up

Introducing correlated continuum wave functions for the two- and re-particle-continuum a microscopic theory of nuclear reactions based on a method of Fano is developed. The S-matrix-elements are given by the matrix-elements between correlated continuum wave functions and bound state wave functions. The antisymmetrization of the continuum wave functions with more than one particle in the continuum is included. The theory can be straightforwardly applied on the n-nucleon-emission process following photo- and particle excitations

An evolutionary approach to constraint-regularized learning

The success of machine learning methods for inducing models from data crucially depends on the proper incorporation of background knowledge about the model to be learned. The idea of constraint-regularized learning is to em- ploy fuzzy set-based modeling techniques in order to express such knowl- edge in a flexible way, and to formalize it in terms of fuzzy constraints. Thus, background knowledge can be used to appropriately bias the learn- ing process within the regularization framework of inductive inference. After a brief review of this idea, the paper offers an operationalization of constraint- regularized learning. The corresponding framework is based on evolutionary methods for model optimization and employs fuzzy rule bases of the Takagi- Sugeno type as flexible function approximators

An evolutionary approach to constraint-regularized learning

The success of machine learning methods for inducing models from data crucially depends on the proper incorporation of background knowledge about the model to be learned. The idea of constraint-regularized learning is to em- ploy fuzzy set-based modeling techniques in order to express such knowl- edge in a flexible way, and to formalize it in terms of fuzzy constraints. Thus, background knowledge can be used to appropriately bias the learn- ing process within the regularization framework of inductive inference. After a brief review of this idea, the paper offers an operationalization of constraint- regularized learning. The corresponding framework is based on evolutionary methods for model optimization and employs fuzzy rule bases of the Takagi- Sugeno type as flexible function approximators

Support Vector Machines and Quad-trees

We present an approach based on Support Vector Machines (SVM) and quad-tree decomposition for compressing still images. Unlike JPEG,themethod applies the discrete cosine transform (DCT) to regions of variable size, re-scale them to a unique block size, and before the coefficients are entropy encoded, they are approximated by a SVM model. The method was applied to grey scale and colour images and the obtained results improved JPEG's performance at medium and low bit rates