Abstract: A discriminative feature set (DFS) selection method is described wherein a forward wrapper framework and a self error-correction concept are used. In this approach, the first feature is selected using a statistical measure. After that, the feature that aims to correct the errors made by the current feature set is selected using a measure called correction score (CS) and is subsequently added into the feature set. This error-corrective feature-adding process stops until a required number of features are included into the DFS or a pre-defined accuracy is achieved. According to different levels of error correction, this method has three derivatives for different tasks and data. The speediness and adaptability of this approach make it efficient and effective for high-dimensional discriminative feature selection.

Abstract: A computer-based artificial network is presented that is capable of learning, recognizing, and generating temporal-spatial sequences. The network system includes time-delays and artificial neural subnetworks. The system generally includes three parts: (1) comparator units, (2) a parallel array of subnetworks and (3) delayed feedback lines from the output of the system to the neural subnetwork layer.