— A key step in many statistical learning methods used in machine learning involves solving a convex optimization problem containing one or more hyper-parameters that must be sel...
Kristin P. Bennett, Jing Hu, Xiaoyun Ji, Gautam Ku...
— The ability to identify protein binding sites and to detect specific amino acid residues that contribute to the specificity and affinity of protein interactions has importan...
—It is proposed that the creation of Artificial General Intelligence (AGI) at the human level and ultimately beyond is a problem addressable via integrating computer science algo...
— Multimodal integration of sensory information has clear advantages for survival: events that can be sensed in more than one modality are detected more quickly and accurately, a...
- A dynamical neural model that is strongly biologically motivated is applied to learning and retrieving binary patterns. This neural network, known as Freeman’s Ksets, is traine...
Abstract— Making inferences and choosing appropriate responses based on incomplete, uncertainty and noisy data is challenging in financial settings particularly in bankruptcy de...
—In this paper, we present three different methods for implementing the Probabilistic Neural Network on a Beowulf cluster computer. The three methods, Parallel Full Training Set ...
Jimmy Secretan, Michael Georgiopoulos, Ian Maidhof...
—A new application of the NN ensemble technique to improve the accuracy and stability of the calculation of NN emulation Jacobians is presented. The term “emulation” is defin...
— The present paper is devoted to the exploration of the properties of the simple spiking neuron model and quantification of the information transfer rate, which the separate neu...
—This paper presents the design of an optimal Auxiliary Transient Neurocontroller (ATNC) for the Gate Controlled Series Capacitor (GCSC) in a multi-machine power system. GCSC is ...