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Optimization-Neural Networks Learning from Data. Theodore B. Trafalis School of Industrial Engineering University of Oklahoma Norman, OK. Why Artificial Neural Networks?. Massive parallelism Distributed representation and computation Learning ability Adaptivity

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optimization neural networks learning from data

Optimization-Neural Networks Learning from Data

Theodore B. Trafalis

School of Industrial Engineering

University of Oklahoma

Norman, OK.

why artificial neural networks
Why Artificial Neural Networks?
  • Massive parallelism
  • Distributed representation and computation
  • Learning ability
  • Adaptivity
  • Inherent contextual information processing
  • Fault tolerance
  • Low energy
challenging problems
Challenging Problems
  • Pattern Classification
  • Clustering/categorization
  • Function approximation
  • Prediction/forecasting
  • Optimization
  • Content-addressable memory
  • Control
neural network components
Neural Network Components
  • Architecture:ANNs can be viewed as weighted directed graphs in which artificial neurons are nodes and directed edges (with weights) are connections between neuron outputs and neuron inputs.
  • Feed-forward networks: graphs have no loops.
  • Recurrent (feedback) networks: loops occur
  • Learning
feed forward architecture
Feed-forward Architecture

We use the following architecture

learning
Learning
  • Supervised: outputs are provided
  • Unsupervised: outputs are not provided
  • Reinforcement: the network is provided with only a critique on the correctness of network outputs, not the correct answers
fundamental issues of learning theory
Fundamental Issues of Learning Theory
  • Sample complexity: what is the number of training patterns needed for valid generalization.
  • Capacity: How many patterns can be stored and what functions and decision boundaries a network can form.
  • Computational complexity: time required for a learning algorithm to estimate a solution from training patterns.
    • Designing efficient algorithms for neural network learning is a very active research.
  • Develop new efficient algorithms.