Layered neural networks are of interest as a tool to implement input-output mappings.
Layered neural networks are of interest as a tool to implement input-output mappings.
The problem of inductive inference refers to extracting general rules, learning concepts from a training set consisting of some fraction of the total number of possible examples of the concept.
Layered neural networks are of interest as a tool to implement input-output mappings.
The problem of inductive inference refers to extracting general rules, learning concepts from a training set consisting of some fraction of the total number of possible examples of the concept.
The problem of inductive inference refers to extracting general rules, learning concepts from a training set consisting of some fraction of the total number of possible examples of the concept.
Learning in layered neural networks is formulated as an iterative reduction of the intrinsic entropy of the chosen network architecture.
A key dimension in personalization of converged (wireless and wireline, web) communications services is adapting each service to a user's context, and thus tailor the services to the daily lives of
Icons, or graphic symbols, have recently become widely available as a means of human-computer interaction.
Coordinated Multi-Point systems appear as advanced promising strategies to improve user throughputs, especially in interference limited regions, at cell edge.
