Narrow Results

A new framework is introduced which allows the formulation of difficult structural classification tasks in terms of decision-theoretic-based pattern recognition. It is based on extending the classical formulation of generalized linear discriminant functions so as to permit each given object to have a different vector representation in each class. The proposed extension properly accounts for the corresponding extension of the classical learning techniques of linear discriminant functions in a way such that the convergence of the extended techniques can still be proved. The proposed framework can be considered as a hybrid methodology in which both structural and decision-theoretic pattern recognition are integrated. Furthermore, it can be considered as a means to achieve convenient tradeoffs between the inductive and deductive ways of knowledge acquisition, which can result in rendering tractable the possibly hard original inductive learning problem associated with the given task. The proposed framework and methods are illustrated through their use in two difficult structural classification tasks, showing both the appropriateness and the capability of these methods to obtain useful results.

A new approach for feature extraction using neural response has been developed in this paper through combining the hierarchical architectures with the sparse coding technique. As far as proposed layered model, at each layer of hierarchy, it concerned two components that were used are sparse coding and pooling operation. While the sparse coding was used to solve increasingly complex sparse feature representations, the pooling operation by comparing sparse outputs was used to measure the match between a stored prototype and the input sub-image. It is recommended that value of the best matching should be kept and discarding the others. The proposed model is implemented and tested taking into account two ranges of recognition tasks i.e. image recognition and speech recognition (on isolated word vocabulary). Experimental results with various parameters demonstrate that proposed scheme leads to extract more efficient features than other methods.