Classics


Machine Learning, Neural and Statistical Classification

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This book (originally published in 1994 by Ellis Horwood) is now out of print. The copyright now resides with the editors who have decided to make the material freely available on the web.This book is based on the EC (ESPRIT) project StatLog which compare and evaluated a range of classification techniques, with an assessment of their merits, disadvantages and range of application. This integrated volume provides a concise introduction to each method, and reviews comparative trials in large-scale commercial and industrial problems. It makes accessible to a wide range of workers the complex issue of classification as approached through machine learning, statistics and neural networks, encouraging a cross-fertilization between these discplines.


The Role of Experimentation in Artificial Intelligence

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Intelligence is a complex, natural phenomenon exhibited by humans and many other living things, without sharply defined boundaries between intelligent and unintelligent behaviour. Artificial inteliigence focuses on the phenomenon of intelligent behaviour, in humans or machines. Experimentation with computer programs allows us to manipulate their design and intervene in the environmental conditions in ways that are not possible with humans. Thus, experimentation can help us to understand what principles govern intelligent action and what mechanisms are sufficient for computers to replicate intelligent behaviours.Phil. Trans. R. Soc. Lond. A. 1994 349 1689


The coming technological singularity: How to survive in the post-human era

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National Aeronautics and Space Administration Office of Management Scientific and Technical Information Program 1993 Into the Era of Cyberspace Our robots precede us with infinite diversity exploring the universe delighting in complexity A matrix of neurons, we create our own reality of carbon and of silicon, we evolve toward what we chose to be. The symposium "Vision 21: Interdisciplinary Science and Engineering in the Era of Cyberspace" was held at the Holiday Inn in Westlake, Ohio on March 30-31, 1993, sponsored by the NASA Lewis Research Center's Aerospace Technology Directorate under the auspices of the NASA Office of Aeronautics, Exploration and Technology. Carol Stoker, of the Telepresence for Planetary Exploration Project at NASA Ames Research Center, is a leading researcher in both telerobotics and Mars. Marc G. Millis NASA Lewis Research Center Cleveland, Ohio Technologies that exist today were once just visions in the minds of their creat


Tight performance bounds on greedy policies based on imperfect value functions

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The U Tree algorithm generates a tree based state discretization that efficiently finds the relevant state chunks of large propositional domains. We have performed experiments in a variety of domains that show that Continuous U Tree effectively handles large continuous state spaces. The Continuous U Tree algorithm described in this paper extends these algorithms to work with continuous state spaces rather than propositional state spaces. In this paper we describe Continuous U Tree, a generalizing, variable-resolution, reinforcement learning algorithm that works with continuous state spaces.


Towards a Reading Coach that Listens: Automated Detection of Oral Reading Errors

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(July 1993). Towards a Reading Coach that Listens: Automated Detection of Oral Reading Errors. (August 1993). Getting Computers to Listen to Children Read: A New Way to Combat Illiteracy (7-minute video). Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page.


C4.5 Programs for Machine Learning

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See also:Bagging, Boosting, and C4.5Improved Use of Continuous Attributes in C4.5Morgan Kaufmann


Learning Problem-Solving Heuristics by Experimentation

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Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. Part II covers important issues affecting the design of learning programs particularly programs that learn from examples. It also describes inductive learning systems. This book is intended for researchers in artificial intelligence, computer science, and cognitive psychology; students in artificial intelligence and related disciplines; and a diverse range of readers, including computer scientists, robotics experts, knowledge engineers, educators, philosophers, data analysts, psychologists, and electronic engineers."


Prioritized sweeping—Reinforcement learning with less data and less time

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We present a new algorithm,prioritized sweeping, for efficient prediction and control of stochastic Markov systems. Incremental learning methods such as temporal differencing and Q-learning have real-time performance. It uses all previous experiences both to prioritize important dynamic programming sweeps and to guide the exploration of state-space. We compare prioritized sweeping with other reinforcement learning schemes for a number of different stochastic optimal control problems.


Neural Network Perception for Mobile Robot Guidance

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Abstract: 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. But real world problems like vision based mobile robot guidance presents a different set of challenges for the connectionist paradigm. Among them are: How to develop a general representation from a limited amount of real training data, How to understand the internal representations developed by artificial neural networks, How to estimate the reliability of individual networks, How to combine multiple networks trained for different situations into a single system, How to combine connectionist perception with symbolic reasoning. Once trained, individual ALVINN networks can drive in a variety of circumstances, including single-lane paved and unpaved roads, and multi-lane lined and unlined roads, at speeds of up to 55 miles per hour.


Approximating probabilistic inference in Bayesian belief networks is NP-hard

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It is known that exact computation of conditional probabilities in belief networks is NP-hard. Many investigators in the AI community have tacitly assumed that algorithms for performing approximate inference with belief networks are of polynomial complexity. However, we have discovered that the general problem of approximating conditional probabilities with belief networks, like exact inference, resides in the NP-hard complexity class. More specifically, we show that the existence of a polynomial-time relative approximation algorithm for major classes of problem instances implies that NP P. We present our proof and explore the implications of the result.