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Topic: Machine learning

Related Topics

Unsupervised learning

Supervised learning

Boosting

Statistical classification

Support vector machine

Inductive bias

Artificial neural network

Active learning

Neural networks

Boltzmann machine

Nearest neighbor (pattern recognition)

Data Mining For Very Busy People

Bayesian network

Algorithm

Naive Bayes classifier

	Machine learning - Wikipedia, the free encyclopedia
		Machine learning overlaps heavily with statistics, since both fields study the analysis of data, but unlike statistics, machine learning is concerned with the algorithmic complexity of computational implementations.
		Machine learning has a wide spectrum of applications including search engines, medical diagnosis, bioinformatics and cheminformatics, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing and robot locomotion.
		Machine learning algorithms are organized into a taxonomy, based on the desired outcome of the algorithm.
	en.wikipedia.org /wiki/Machine_learning (1216 words)

	Machine Learning
		BLAKE, C.L. and C.J. MERZ, UCI repository of machine learning databases, 1998.
		BLAKE, C.L. and C.J. UCI Repository of machine learning databases [http://www.
		BLAKE, C.L. and C.J. UCI repository of machine learning databases.
	www.machinelearning.net (430 words)

	The Machine Learning Dictionary
		Learning in backprop seems to operate by first of all getting a rough set of weights which fit the training patterns in a general sort of way, and then working progressively towards a set of weights that fit the training patterns exactly.
		An instance is a term used in machine learning particularly with symbolic learning algorithm, to describe a single training item, usually in the form of a description of the item, along with its intended classification.
		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.
	www.cse.unsw.edu.au /~billw/mldict.html (6457 words)

	UCI Machine Learning Group (Site not responding. Last check: 2007-11-04)
		Machine learning investigates the mechanisms by which knowledge is acquired through experience.
		New learning algorithms often result from research into the effect of problem properties on the accuracy and run-time of existing algorithms.
		We investigate learning from structured databases (for applications such as screening loan applicants), image data (for applications such as character recognition), and text collections (for applications such as locating relevant sites on the World Wide Web).
	www.ics.uci.edu /~mlearn (139 words)

	Machine Learning
		You will have a knowledge of the strengths and weaknesses of different machine learning algorithms (relative to the characteristics of the application domain) and be able to adapt or combine some of the key elements of existing machine learning algorithms to design new algorithms as needed.
		You will have an understanding of the current state of the art in machine learning and be able to begin to conduct original research in machine learning.
		Machine Learning - Com S 573 - is a 3-credit, introductory graduate course on Machine Learning offered by the Department of Computer Science at Iowa State University.
	www.cs.iastate.edu /~cs573x/homepage.html (857 words)

	Machine Learning
		But 'machine learning,' as it's often called, has remained mostly the province of academic researchers, with only a few niche applications in the commercial world, such as speech recognition and credit card fraud detection.
		Machine learning of grammars finds a variety of applications in syntactic pattern recognition, adaptive intelligent agents, diagnosis, computational biology, systems modelling, prediction, natural language acquisition, data mining and knowledge discovery.
		Machine Learning is a scientific field addressing the question 'How can we program systems to automatically learn and to improve with experience?' We study learning from many kinds of experience, such as learning to predict which medical patients will respond to which treatments, by analyzing experience captured in databases of online medical records.
	www.aaai.org /AITopics/html/machine.html (3442 words)

	Machine Learning (Site not responding. Last check: 2007-11-04)
		Lecture 19: Learning in the presence of noise.
		Lecture 21: Learning decision trees using the fourier spectrum (in the membership query model, with respect to the uniform distribution).
		Learning DNF with membership queries with respect to the uniform distribution.
	theory.lcs.mit.edu /~mona/lectures.html (192 words)

	Abstracts
		The University of Pennsylvania has a large and active group in machine learning.
		Programs of study can be pursued in the theory, algorithms, or applications of machine learning, as well as any combination thereof.
		Machine learning is important for understanding this because perception depends on exploiting subtle patterns in the visual input, that deriving from statistical regularities in the world."
	learning.cis.upenn.edu (1095 words)

	machine learning framework: Creating Understandable Computational Models from Data
		machine learning framework is a complete solution for business and financial engineers, process and manufacturing engineers, quality assurance professionals, and all experts who want to extract computational models from data.
		Knowledge engineers and machine learning experts, and others who search for a framework to develop customized solutions, will also benefit from its future-looking fuzzy variants of machine learning algorithms in an open architecture.
		Optimized future-looking, fuzzy-logic-based machine learning methods and algorithms implemented in C++ are integrated into Mathematica's high-level symbolic computation, visualization, and programming environment.
	www.wolfram.com /products/applications/mlf (332 words)

	Machine Learning
		Learning Bayesian network parameters in the presence of missing attribute values (using Expectation Maximization) when the structure is known; Learning networks of unknown structure in the presence of missing attribute values.
		Tractable Learning of Large Bayes Net Structures from Sparse Data, Goldernberg, A. and Moore, A. In Proceedings of the International Conference on Machine Learning, 2004.
		Q learning algorithm for learning optimal policies when an accurate model of the environment is not known.
	www.cs.iastate.edu /~cs573x/studyguide05.html (3959 words)

	Machine Learning (Theory) (Site not responding. Last check: 2007-11-04)
		The source of machine translation success seems to be a combination of better models (switching to phrase-based translation made a huge leap), application of machine learning technology, and big increases in the quantity of data available.
		One of the questions facing machine learning as a field is “Can we produce a generalized learning system that can solve a wide array of standard learning problems?” The answer is trivial: “yes, just have children”.
		This is the answer that machine learning would like to hear because it agrees with the hypothesis that a simple general learning system exists.
	hunch.net (6025 words)

	UCI Machine Learning Repository (Site not responding. Last check: 2007-11-04)
		This is a repository of databases, domain theories and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms.
		The majority of the entries in the repository were contributed by machine learning researchers outside of UCI.
		We also maintain a list of other machine learning sites.
	www.ics.uci.edu /~mlearn/MLRepository.html (293 words)

	CMPUT 466/551 Home Page
		Learning -- ie, using experience to improve performance -- is an essential component of intelligence.
		The field of Machine Learning, which addresses the challenge of producing machines that can learn, has become an extremely active, and exciting area, with an ever expanding inventory of practical (and profitable!) results, many enabled by recent advances in the underlying theory.
		This course provides a (near)graduate-level introduction to the field, with an emphasis on the design on agents that can learn about their environment, to help them improve their performance on a range of tasks.
	www.cs.ualberta.ca /~greiner/C-466 (573 words)

	Machine Learning: Course Description
		Machine Learning is the study of how to build computer systems that learn from experience.
		The course will explain how to build systems that learn and adapt using real-world applications from industry and science (e.g., learning to classify astronomical objects, to predict medical diagnoses, to play chess).
		The project will be a report on some area in machine learning you find most interesting.
	www2.cs.uh.edu /~vilalta/courses/machinelearning/machinelearning.html (269 words)

	Introduction to Machine Learning - The MIT Press (Site not responding. Last check: 2007-11-04)
		Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data.
		Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts.
		After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.
	mitpress.mit.edu /catalog/item?ttype=2&tid=10341 (265 words)

	Machine Learning and Information Retrieval (Belew/Shavlik)
		It is currently being maintained by Rik Belew as a resource in support of the AAAI Spring Symposium on Machine Learning in Information Access to be held at Stanford, March 25-27, 1996.
		WebWatcher: Machine learning and hypertext, to appear in Fachgruppentreffen Maschinelles Lernen, Dortmund, Germany, August 1995.
		Learning the optimal parameters in a ranked retrieval system using multi-query relevance feedback, by B. Bartell, G. Cottrell, and R. Belew.
	www-cse.ucsd.edu /users/rik/MLIA.html (819 words)

	Amazon.com: Machine Learning: Books: Thomas Mitchell (Site not responding. Last check: 2007-11-04)
		Machine Learning serves as a useful reference tool for software developers and researchers, as well as an outstanding text for college students.
		First of all, the statistical part of machine learning is JUST a real subset of mathematical statisitcs, whatever Bayesian or frequentist.
		We have used it as a textbook for a half-year course of Machine Learning.
	www.amazon.com /exec/obidos/tg/detail/-/0071154671?v=glance (1221 words)

	Journal of Machine Learning Gossip
		The mission of the Journal of Machine Learning Gossip (JMLG) is to provide an archival source of important information that is often discussed informally at conferences but is rarely, if ever, written down.
		It is designed to be open and accessible for all researchers, but with particular emphasis on providing guidance and advice to the next generation of machine learning researchers: we do not wish them to take as long to discover the wisdom in these pages as the editorial board did.
		The Journal of Machine Learning Gossip is a democratic publication and therefore has no hierarchical structure in its editorial board.
	www.jmlg.org (115 words)

	Machine Learning - Autocomplete
		Setting this preference will enable a very simple learning algorithm that will identify urls that you are likely to click on in the autocomplete drop down list.
		We are very excited about applying machine learning and artificial intelligence ideas to make Mozilla a more "intelligent" browser.
		We started working on autocomplete as a first, concrete step towards applying machine learning to Mozilla because we think that the work we do here can be broadly applied.
	www.mozilla.org /projects/ml/autocomplete (1403 words)

	Machine Learning at UC Santa Cruz (Site not responding. Last check: 2007-11-04)
		The Machine Learning group at UCSC is dedicated to the discovery and analysis of algorithms for different learning problems.
		Over the past eight years we have been developing a new family of on-line learning algorithms with qualitatively different behavior than the previously known gradient descent techniques.
		There is also a large group applying machine learning techniques to computational biology.
	www.soe.ucsc.edu /research/ml (104 words)

	News Indexed by Topic - MACHINE LEARNING
		His 'invention machine,' as he likes to call it, has even earned a U.S. patent for developing a system to make factories more efficient, one of the first intellectual-property protections ever granted to a nonhuman designer.
		Some of the more advanced forms of machine learning enable new hypotheses, in the form of logical rules and principles, to be extracted relative to predefined background knowledge.
		This is because the device rapidly learns to recognise activity in the area of a person's motor cortex, the area of the brain associated with movement.
	www.aaai.org /AITopics/newstopics/machine.html (8770 words)

	Machine Learning and Applied Statistics - Home
		The Machine Learning and Applied Statistics (MLAS) group is focused on learning from data and data mining.
		By building software that automatically learns from data, we enable applications that (1) do intelligent tasks such as handwriting recognition and natural-language processing, and (2) help human data analysts more easily explore and better understand their data.
		We strive to advance the state of the art in machine learning and statistics, develop fast scalable algorithms for learning and mining, implement portions our work toolkits, and apply our work to numerous product applications.
	research.microsoft.com /mlas (799 words)

	Machine Learning Courses
		Machine Learning, Carlos Guestrin and Tom Mitchell, Carnegie Mellon University.
		Introduction to Machine Learning, Ammon Shashua, Hebrew University of Jerusalem.
		Machine Learning and Inductive Inference Hendrik Blockheed, Katholieke Universiteit Leuven, Belgium
	www.cs.iastate.edu /~honavar/Courses/cs673/machine-learning-courses.html (396 words)

	Machine Learning Project
		An exciting and potentially far-reaching development in computer science is the invention and application of methods of machine learning.
		The overall goal of our project is to build a state-of-the-art facility for developing machine learning (ML) techniques and to apply them to real-world data mining problems.
		Our machine learning package is publically available and presents a collection of algorithms for solving real-world data mining problems.
	www.cs.waikato.ac.nz /~ml (261 words)

	Category:Machine learning - Wikipedia, the free encyclopedia
		Machine learning is a branch of statistics and computer science, which studies algorithms and architectures that learn from observed facts.
		The main article for this category is Machine learning.
		There are 5 subcategories shown below (more may be shown on subsequent pages).
	en.wikipedia.org /wiki/Category:Machine_learning (88 words)

	CS 661 - Machine Learning (Salzberg)
		This is an advanced course that will focus on the recent literature on the application of machine learning to problems from a range of different areas, including biology, astronomy, and informational retrieval.
		Links to Researchers in Machine Learning, pointers to hundreds of individual researchers maintained by David Aha at NRL.
		Machine learning research at The University of Texas at Austin
	www.cs.jhu.edu /~salzberg/cs661.html (893 words)

	COLT: Computational Learning Theory
		Computational Learning Theory (COLT) is a research field devoted to studying the design and analysis of algorithms for making predictions about the future based on past experiences.
		The annual Conference on Computational Learning Theory began in 1988; the European Conference on Computational Learning Theory and the Workshop on Algorithmic Learning Theory were formed soon after.
		COLT has strongly encouraged interaction with other fields that work on problems of prediction such as applied machine learning, statistics, information theory, pattern recognition and statistical physics, as well as other areas of computer science such as artificial intelligence, complexity theory and cryptography.
	www.learningtheory.org (163 words)

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