Constructive Neural Network Learning Algorithms

Constructive learning algorithms offer an attractive approach for the incremental construction of near-minimal neural-network architectures for pattern classification. They help overcome the need for ad hoc and often inappropriate choices of network topology in algorithms that search for suitable weights in a priori fixed network architectures. Several such algorithms are proposed in the literature and shown to converge to zero classification errors (under certain assumptions) on tasks that involve learning a binary to binary mapping (i.e., classification problems involving binary-valued input attributes and two output categories). We present two constructive learning algorithms MPyramid-real and MTiling-real that extend the pyramid and tiling algorithms, respectively, for learning real to M-ary mappings (i.e., classification problems involving real-valued input attributes and multiple output classes). We prove the convergence of these algorithms and empirically demonstrate their appli...

• Type: Conference
• Year: 1996
• Where: AAAI
• Authors: Rajesh Parekh, Jihoon Yang, Vasant Honavar
• Keywords: AAAI 1996, Classification Problems, Constructive Learning Algorithms, Intelligent Agents, Pattern Classification