Neural Networks
Neural Network Perception for Mobile Robot Guidance
Vision based mobile robot guidance has proven difficult for classical machine vision methods because of the diversity and real time constraints inherent in the task. This thesis describes a connectionist system called ALVINN (Autonomous Land Vehicle In a Neural Network) that overcomes these difficulties. ALVINN learns to guide mobile robots using the back-propagation training algorithm. Because of its ability to learn from example, ALVINN can adapt to new situations and therefore cope with the diversity of the autonomous navigation task. But real world problems like vision based mobile robot guidance presents a different set of challenges for the connectionist paradigm.
Extracting refined rules from knowledge-based neural networks
Machine Learning 13 (1), 71-101, 1993.
A practical Bayesian framework for back-propagation networks
Neural Computation, 4 (3), 448-72.
Scheduling with neural networks: The case of the Hubble space telescope
Johnston, M. D. | Adorf, H.-M.
Computers and Operations Research, 19 (3-4), 209-240.
Symbolic and neural learning algorithms: An experimental comparison
Shavlik, J. W. | Mooney, R. | Towell, G. G.
Machine Learning, 6(1): 111-143.
Interactions between learning and evolution
Ackley, D. H. | Littman, M. L.
In Langton, C., Taylor, C., Farmer, J. D., and Ramussen, S. (Eds.), Artificial Life II, pp. 487–509. Addison-Wesley
Neural Network Design and the Complexity of Learning
MIT Press. See also: A reply to Honavar's book review of Neural Network Design and the Complexity of Learning (https://link.springer.com/article/10.1007%2FBF00993256?LI=true).
Computer Systems that Learn: Classification and Prediction Methods from Statistics
Weiss, S. M., Kulikowski, C. A.
Full text available for a fee. This book is a practical guide to classification learning systems and their applications. These computer programs learn from sample data and make predictions for new cases, sometimes exceeding the performance of humans. Practical learning systems from statistical pattern recognition, neural networks, and machine learning are presented. The authors examine prominent methods from each area, using an engineering approach and taking the practitioner's viewpoint. Intuitive explanations with a minimum of mathematics make the material accessible to anyone--regardless of experience or special interests. The underlying concepts of the learning methods are discussed with fully worked-out examples: their strengths and weaknesses, and the estimation of their future performance on specific applications. Throughout, the authors offer their own recommendations for selecting and applying learning methods such as linear discriminants, back-propagation neural networks, or decision trees. Learning systems are then contrasted with their rule-based counterparts from expert systems.Morgan Kaufmann, 1990
A parallel network that learns to play backgammon
Artificial Intelligence, 39 (3), 357-90.