Publications

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Learning and Generalization in Layered Neural Networks The Contiguity Problem

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 and Generalization in Layered Neural Networks: The Contiguity Problem

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 and Generalization in Layered Neural Networks: The Contiguity Program.

Learning in layered neural networks is formulated as an iterative reduction of the intrinsic entropy of the chosen network architecture.

Learning and Managing User Context in Personalized Communications Services

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

Learning and Preference for Icon-Based Interface

Icons, or graphic symbols, have recently become widely available as a means of human-computer interaction.

Learning based spectral clustering for LTE Downlink CoMP Systems

Coordinated Multi-Point systems appear as advanced promising strategies to improve user throughputs, especially in interference limited regions, at cell edge.

Learning constraints through partial queries

Learning constraint networks is known to require a number of membership queries exponential in the number of variables.

Learning Contiguity with Layered Neural Networks

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 Contiguity with Layered Neural Networks

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 Diagnostic Knowledge for Continuous-Variable Dynamic Systems

Continuous-variable dynamic systems, such as chemical refineries, electrical power plants, and the human cardiovascular system, exhibit time varying behavior through continuous changes in parameter