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Bibliographic Details

Title: Neural Networks for Pattern Recognition (...

Publisher: Clarendon Press

Publication Date: 1996

Binding: Soft cover

Condition: acceptable

About this title

Synopsis:

The first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition.

Review:

This book provides a solid statistical foundation for neural networks from a pattern-recognition perspective. The focus is on the types of neural nets that are most widely used in practical applications, such as the multi-layer perceptron and radial basis function networks. Rather than trying to cover many different types of neural networks, Christopher Bishop thoroughly covers topics such as density estimation, error functions, parameter optimisation algorithms, data pre-processing and Bayesian methods. All topics are organised well and all mathematical foundations are explained before being applied to neural networks. The text is suitable for a graduate or advanced undergraduate level course on neural networks or for practitioners interested in applying neural networks to real-world problems. The reader is assumed to have the level of mathematical knowledge necessary for an undergraduate science degree. --Jake Bond

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Goodwill of Silicon Valley?s mission is to support our employees, our customers, and people with challenging barriers to employment; to raise their standard of living and improve their lives through our services and social enterprise. Founded in Santa Clara County in 1928, Goodwill of Silicon Valley is dedicated to improving employment opportunities, increasing standards of living, providing economic independence, and restoring our clients? sense of self-worth. We do this through workforce creation, vocational training, and environmental stewardship. With 18 retail stores, an online store, an extensive reuse/recycling operation, and our Contract Services division, we help people overcome barriers to employment, build sustainable livelihoods, and transform their lives and communities.

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