Machine learning

Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases.

Machine learning
Machine learning is an area of artificial intelligence involving developing techniques to allow computers to "learn".

Machine Learning Introductory Overview
Machine Learning includes a number of advanced statistical methods for handling regression and classification tasks with multiple dependent and independent variables.

Machine Learning in Games Development
In this article, I shall outline the current perceptions of ‘Machine Learning' in the games industry, some of the techniques and implementations used in current and future games, ...

What is Machine Learning?
To solve problems computers require intelligence. Learning is central to intelligence. As intelligence requires knowledge, it is necessary for the computers to acquire knowledge. Machine learning serves this purpose.

Machine learning (2 x 16F877)
By Bert van Dam
Nothing is more fascinating than learning machines. Well, except of course... never mind.

Machine Learning - An Introduction
Introduction
Machine Learning is simply the ability of the machine to learn from the previous experience or history and perform better at a given task, as the future mimics the past.

Machine learning: driving significant improvements in biometric performance ...

machine learning Machine learning is said to occur in a program that can modify some aspect of itself, often referred to as its state, so that on a subsequent execution with the same input, a different (hopefully better) output is produced.

Robots in the Classroom: Sejnowski on Machine Learning and Education
[ Artificial Intelligence, Developmental Psychology, Link Posts ]
Posted on: September 2, 2009 4:05 PM, by Chris Chatham ...

Machine Learning and Knowledge Acquisition: learning from memorization, examples, explanation, and exploration. learning nearest neighbor, naive Bayes, and decision tree classifiers, Q-learning for learning action policies, applications.

Machine learning
Machine learning deals with making computers evolve certain behaviors based on current and past behaviors' results.

Machine Learning Methods:Several methods for inductive learning have been developed under the common label "Machine Learning". All these methods use a set of samples to generate an approximation of the underling function that generated the data.

Machine Learning
Machine learning refers to the ability of computers to automatically acquire new knowledge, learning from, for example, past cases or experience, from the computer's own experiences, or from exploration.

machine learning study of ways by which a program can automatically modify its knowledge and procedures to improve its performance, based on explicit instruction from a human teacher, training examples, its experiences, ...

Machine Learning by Tom M. Mitchell
Pattern Recognition and Machine Learning by Christopher M. Bishop
Data Mining: Practical Machine Learning Tools and Techniques by Eibe Frank ...

Machine learning:
Having a computer program itself from a set of examples so you don't have to program it yourself. This will be a strong focus of this course: neural networks that learn from a set of examples.

Machine Learning. Section 1.2.8 of Chapter One (available online) of George F. Luger's textbook, Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 5th Edition (Addison-Wesley; 2005).

In machine learning, a decision tree is a predictive model; that is, a mapping of observations about an item to conclusions about the item's target value.

machine learning
knowledge discovery
kdd
clustering
Definition: Data mining is the use of automated data analysis techniques to uncover previously undetected relationships among data items.

Main article: machine learning
As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to learn.

Another model of machine learning is based on the biological system of genetics, in which systems change over time. Introduced by John Henry Holland at MIT in the 1950s, this kind of system uses genetic algorithms.

Samuel, "Some studies in machine learning using the game of checkers," IBM J. Res. Dev., vol. 3, pp. 211-219, July 1959.
[03] C. E. Shannon, "Programming a digital computer for playing chess, " in [K].
[04] C. E.

The Decision Tree is one of the most popular classification algorithms in current use in Data Mining and Machine Learning.

Empirical and theoretical results in machine learning suggest that less complex models are often better at generalizing to unseen cases in situations in which the data is sparse.

Irvine Machine Learning programs.
MLC++: a machine learning library. C++.
List of public domain software maintained by Matt Ginsberg. Common Lisp, Prolog.
CMU AI Repository of software packages.

The artificial ones are computer programs implementing sophisticated pattern detection and machine learning algorithms on a computer to build predictive models from large historical databases.

The field goes by many names, such as connectionism, parallel distributed processing, neuro-computing, natural intelligent systems, machine learning algorithms, and artificial neural networks.

(machine) learning
Machine learning is one of the fundamental areas of Artificial Intelligence. It concerns systems that learn new knowledge on the basis of observed examples.
[close the glossary] ...

In: Working Papers of the AAAI Spring Symposium on Machine Learning of Natural Language and Ontology (DW Powers & L Reeker, Eds.) pp. 65-74.

Genetic Algorithms in Search Optimization and Machine Learning, Addison Wesley Publishing Co.Inc: Reading, MA, 1989
Schöneburg E., Heinzmann F., Feddersen S., Genetische Algorithmen und Evolutionsstrategien, Addison Wesley ISBN 3-89319-493-2, 1994 ...

"Selecting relevant features in machine learning"
"Feature Selection Evaluation"
"Toward Optimal Feature Selection"
Alexander Koltunov's bibliography
Keith Price's bibliography ...

* "Learning to Predict by the Methods of Temporal Differences" by Rich Sutton, Machine Learning 3: 9--44, is available by ftp from the University of Massachusetts. Other related papers are available via Rich Sutton's Publications page.

Genetic algorithms has been used for difficult problems (such as NP-hard problems), for machine learning and also for evolving simple programs. They have been also used for some art, for evolving pictures and music.

[Mit97] is a comprehensive undergraduate text on machine learning. Programs can only learn what facts or behaviors their formalisms can represent, ...

Much of what is currently labeled AI research follows a functional model, applying particular programming techniques, such as knowledge engineering, fuzzy logic, genetic algorithms, neural networking, heuristic searching, and machine learning via ...

See also: Artificial intelligence, Knowledge, Neural network, AI, Data mining