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From: "Sergio Navega" <snavega@ibm.net>
Subject: Re: Methods of machine learning ?
Date: 18 Nov 1998 00:00:00 GMT
Message-ID: <3652f8fc.0@news3.ibm.net>
References: <72s716$tu8$1@nnrp1.dejanews.com>
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jazf@my-dejanews.com wrote in message <72s716$tu8$1@nnrp1.dejanews.com>...
>I am currently researching a report on the developmentof artificial
>intelligence. I am looking for a summary of themethods and techniques used
>in machine learning. Cananyone assist ?
>
You didn't say the size of the summary you wanted, so I thought you may
like this very reduced, quick-and-dirty one:
Concept Learning
From sets of positive and negative examples, derive general categories
Examples: Candidate-elimination algorithm,
Unbiased Inductive Learner
Decision Trees
Methods to approximate functions over discrete values with good
resistance to noise and able to learn disjunctive expressions
Examples: ID3, C4.5
Artificial Neural Networks
Learning of real-valued or discrete-valued functions from examples
Also vector-valued functions.
Examples: Perceptrons, Feedforward, Backpropagation, Recurrent Nets, etc
Bayesian Learning
Probabilistic approach inferring distributions from observed data
Examples: Brute-force Bayes Concept learning,
Gibbs algorithm, naive Bayes
Classifier,
Bayesian Belief Network
Instance-Based learning
Training examples are stored and generalization occurs only when
new instances must be classified
Examples: k-Nearest Neighbor, Locally weighted regression,
Radial Basis Functions (also a
method of ANNs)
Learning Sets of Rules
Procedures for learning sets of if-then rules
Examples: FOIL, FOCL, ILP
Reinforcement Learning
Learning is done by performing actions in one environment and
receiving, usually by a trainer, a reward or punishment based
on the appropriateness of the action taken.
Other Methods
Genetic Algorithms: Learning based on simulation of evolutionary processes
EBL: Explanation-Based Learning
KBANN: Knowledge-Based Artificial Neural Networks
and others...
Sergio Navega.
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