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Input Feedback Networks: Classification and Inference Based on Network Structure
 

Summary: Input Feedback Networks: Classification
and Inference Based on Network Structure
Tsvi Achler and Eyal Amir
Department of Computer Science, University of Illinois at Urbana-Champaign
We present a mathematical model of interacting neuron- like units that we call
Input Feedback Networks (IFN). Our model is motivated by a new approach to
biological neural networks, which contrasts with current approaches (e.g. Layered
Neural Networks, Perceptron, etc.). Classification and reasoning in IFN are
accomplished by an iterative algorithm, and learning changes only structure.
Feature relevance is determined during classification. Thus it emphasizes network
structure over edge weights. IFNs are more flexible than previous approaches.
In particular, integration of a new node can affect the outcome of existing nodes
without modifying their prior structure. IFN can produce informative responses to
partial inputs or when the networks are extended to other tasks. It also enables
recognition of complex entities (e.g. images) from parts. This new model is
promising for future contributions to integrated human-level intelligent
applications due to its flexibility, dynamics and structural similarity to natural
neuronal networks.
Introduction
Regulation through feedback is a common theme found in biology including gene

  

Source: Amir, Eyal - Department of Computer Science, University of Illinois at Urbana-Champaign

 

Collections: Computer Technologies and Information Sciences