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Topic: Unsupervised learning

Related Topics

Machine learning

Supervised learning

Artificial neural network

Statistical classification

Support vector machine

Boltzmann machine

Nearest neighbor (pattern recognition)

Set

Bayesian inference

Subset

Random variable

Linear discriminant analysis

Nearest neighbour algorithm

Maximum likelihood

Maximum likelihood estimation

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	Supervised vs. unsupervised learning
		In unsupervised learning, all the observations are assumed to be caused by latent variables, that is, the observations are assumed to be at the end of the causal chain.
		In supervised learning, one set of observations, called inputs, is assumed to be the cause of another set of observations, called outputs, while in unsupervised learning all observations are assumed to be caused by a set of latent variables.
		The difficulty of the learning task increases exponentially in the number of steps between the two sets and that is why supervised learning cannot, in practice, learn models with deep hierarchies.
	www.cis.hut.fi /harri/thesis/valpola_thesis/node34.html (501 words)

	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 algorithms are organized into a taxonomy, based on the desired outcome of the algorithm.
		One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate the behavior of) a function which maps a vector
	en.wikipedia.org /wiki/Machine_learning (1084 words)

	Unsupervised learning - Wikipedia, the free encyclopedia
		Unsupervised learning is a method of machine learning where a model is fit to observations.
		Unsupervised learning is also useful for data compression: fundamentally, all data compression algorithms either explicitly or implicitly rely on a probability distribution over a set of inputs.
		Another form of unsupervised learning is clustering, which is sometimes not probabilistic.
	en.wikipedia.org /wiki/Unsupervised_learning (196 words)

	Zoubin Ghahramani
		NIPS 1999 Tutorial on Probabilistic Models for Unsupervised Learning
		Statistical Approaches to Learning and Discovery, Spring 2002, CMU
		Workshop on Learning Theoretic and Bayesian Inductive Principles, London, July 19-21, 2004
	learning.eng.cam.ac.uk /zoubin (210 words)

	Learning
		Used in supervised learning, a training set is a set of problem instances (described as a set of properties and their values), together with a classification of the instance.
		Learning in which the data structure is a set of nodes connected by weighted links, each node passing a 0 or 1 to other links depending on whether a function of its inputs reaches its activation level.
		The reason reinforcement learning is harder than supervised learning is that the agent is never told what the right action is, only whether it is doing well or poorly, and in some cases (such as chess) it may only receive feedback after a long string of actions.
	www.cs.dartmouth.edu /~brd/Teaching/AI/Lectures/Summaries/learning.html (9712 words)

	Unsupervised learning: Facts and details from Encyclopedia Topic (Site not responding. Last check: )
		Machine learning is an area of artificial intelligence concerned with the development of techniques which allow computers to "learn"....
		Supervised learning is a machine learning technique for creating a function from training data....
		Unsupervised learning is also useful for data compression data compression quick summary:
	www.absoluteastronomy.com /encyclopedia/u/un/unsupervised_learning.htm (992 words)

	Web Project #1 -- Unsupervised Learning (Site not responding. Last check: )
		Unsupervised Learning is a broad term used to classify any type of learning that is conducted without any external interference.
		The main difference between supervised learning and unsupervised learning is that both the inputs and desired outputs are given to the neural network in the former.
		As was the goal with decision trees, unsupervised learning is often used to classify data.
	www.cs.brandeis.edu /~cs113/classprojects/~jlittman/cs113/Web_Project__231_--_Unsupervised_Learning.html (910 words)

	What does unsupervised learning learn? (Site not responding. Last check: )
		In fact, for most varieties of unsupervised learning, the targets are the same as the inputs (Sarle 1994).
		Unsupervised learning is very useful for data visualization (Ripley 1996), although the NN literature generally ignores this application.
		Hebbian learning minimizes the same error function as an auto-associative network with a linear hidden layer, trained by least squares, and is therefore a form of dimensionality reduction.
	www.faqs.org /faqs/ai-faq/neural-nets/part2/section-22.html (1339 words)

	Unsupervised Learning of the Morphology of a Natural Language (Site not responding. Last check: )
		It performs unsupervised learning in the sense that the program's sole input is the corpus; we provide the program with the tools to analyze, but no dictionary and no morphological rules particular to any specific language.
		Their project apparently shares with the present one the requirement that the automatic learning algorithm be responsible for the decision as to which letters constitute the stem and which are part of the suffix(es), though the details offered by Szeroski and Erjavec are sketchy as to how this is accomplished.
		It is not clear from their description whether the base form that they supply is a surface form from a particular point in the inflectional paradigm (the nominative singular), or a more articulated underlying representation in a generative linguistic sense; the former appears to be their policy.
	humanities.uchicago.edu /faculty/goldsmith/Linguistica2000/Paper/paper.html (7872 words)

	Unsupervised Learning of Word Segmentation Rules (York)
		The results of the tests show that a set of rules for word segmentation can be learned from a limited amount of unanotated words, as opposed to other approaches where very large and/or tagged corpora are used.
		Unsupervised learning of naïve morphology with genetic algorithms.
		Unsupervised learning of word segmentation rules with genetic algorithms and inductive logic programming.
	www-ai.ijs.si /~ilpnet2/apps/wseg.html (656 words)

	Unsupervised learning
		Figure: Three different unsupervised learning techniques applied to handwritten images of the digit "two." NMF learns a representation based upon the strokes of the digits, while VQ and PCA learn holistic representations.
		In contrast, vector quantization (VQ) is another unsupervised learning technique that categorizes the input data in terms of prototypes rather than orthogonal basis vectors.
		As seen in the figure, nonnegative matrix factorization (NMF) learns to decompose the image data into their constituent parts, corresponding to the different strokes of a ``two.'' To approximate a particular two, the appropriate strokes are summed together to form the reconstruction.
	www.usenix.org /events/es99/full_papers/lee/lee_html/node11.html (1058 words)

	Semidefinite Embedding (Site not responding. Last check: )
		Overall, the different algorithms for manifold learning in Table II should be viewed as complementary; each has its own advantages and disadvantages.
		Along with the growing interest in manifold learning, the last few years have also witnessed an explosion of interest in kernel methods for pattern recognition [17].
		First, it is based on variance maximization, as opposed to margin maximization [17,29]; the former applies to unsupervised learning, whereas the latter requires (at least some) labeled examples.
	www.seas.upenn.edu /~kilianw/sde (5889 words)

	B553: Unsupervised Hebbian Learning (Site not responding. Last check: )
		Simple Hebbian learning is the right idea (frequent input patterns have the most influence and produce the largest output), but the weights grow without bound.
		Learning rule similar to Oja's, except that weighted outputs up to the unit in question are subtracted out.
		Architecture and learning rule in which outputs go from low selectivity to high selectivity (responding to a small number of inputs).
	www.cs.indiana.edu /classes/b553/unsup1.html (482 words)

	An Unsupervised Learning Rule for the Pulsed Neuron Model: The Vector Quantization of the Auditory Temporal Signals (Site not responding. Last check: )
		However, when it comes to giving input signals directly to the PN models with the supervised learning rules, it is not reasonable because each pulse train of input signals varies its pattern frequently and also the volume of the data is enormous.
		Therefore, before the supervised learning rules are carried out, the information of the input signals needs to be compressed in some ways.Accordingly, in this paper, we propose the unsupervised learning rules and the method of the vector quantization for the PN models to compress the temporal information per every instantaneous time.
		In the current neural networks, the unsupervised learning rules are widely employed for the vector quantization, dimensionality reduction, self-organization, etc. For the prospective application of the PN models, it is significant to establish the unsupervised learning rules for the models.
	csdl2.computer.org /persagen/DLAbsToc.jsp?resourcePath=/dl/proceedings/&toc=comp/proceedings/ijcnn/2000/0619/03/0619toc.xml&DOI=10.1109/IJCNN.2000.861317 (519 words)

	[No title]
		Normally, the success of unsupervised learning hinges on some appropriately designed network which encompasses a task-independent criterion of the quality of representation that the network is required to learn.
		In the remainder of this chapter, some basic unsupervised learning rules for a single unit and for simple networks are introduced.
		In this section, we apply unsupervised Hebbian learning in a simple network setting to extract the m principal directions of a given set of data (i.e., the leading eigenvector directions of the input vectors' auto-correlation matrix).
	neuron.eng.wayne.edu /tarek/MITbook/chap3/3_3.html (3101 words)

	Journal of Vision - Unsupervised learning of visual structure, by Hiles, Intrator, & Edelman
		If humans use such criteria in learning structured objects, they would tend to lump together a pair of highly interdependent fragments, perceiving them as a single shape.
		We tested this hypothesis in two experiments involving a part verification task, in which subjects are known to detect a unitary probe embedded in a larger target faster than a composite one.
		To elucidate the mechanisms behind this kind of unsupervised learning, we developed a computational model of visual structure acquisition, which accepts the same stimuli seen by the human subjects, and exhibits similar patterns of behavior.
	journalofvision.org /2/7/74 (321 words)

	Unsupervised Learning (Site not responding. Last check: )
		The best results for unsupervised learning are, currently, from my MK10 and SNPR models of first language learning.
		SP70 (described below) is a relatively new model of unsupervised learning that attempts to integrate learning with such things as parsing and production of language, fuzzy pattern recognition and best-match information retrieval, probabilistic and exact forms of reasoning, and others.
		As a model of learning, it is not yet as successful as the earlier models.
	www.cognitionresearch.org.uk /papers/ul/ul.htm (228 words)

	ACL'99 Workshop -- Unsupervised Learning in Natural Language Processing -- Call for papers
		Many of the successes achieved from using learning techniques in natural language processing (NLP) have utilized the supervised paradigm, in which models are trained from data annotated with the target concepts to be learned.
		Unsupervised learning utilizes raw, unannotated data to discover underlying structure giving rise to emergent patterns and principles.
		Unsupervised and weakly supervised methods have been used successfully in several areas of NLP, including acquiring verb subcategorization frames (Brent, 1993; Manning, 1993), part-of-speech tagging (Brill, 1997), word sense disambiguation (Yarowsky, 1995), and prepositional phrase attachment (Ratnaparkhi, 1998).
	ling.ucsd.edu /~kehler/unsup-acl-99-cfp.html (583 words)

	NIPS*98 Workshop - Integrating Supervised and Unsupervised Learning
		This workshop debated the relationship between supervised and unsupervised learning.
		The debate was fun because some attendees believe supervised learning has clear advantages, while others believe unsupervised learning is the only game worth playing in the long run.
		One goal for the workshop is to take existing methods that do both supervised and unsupervised learning and "plot" them along a few important dimensions.
	www.cs.cmu.edu /~mccallum/supunsup (333 words)

	Peter Dayan: Publications by Date (Site not responding. Last check: )
		Temporal difference learning model accounts for responses in human ventral striatum and orbitofrontal cortex during Pavlovian appetitive learning.
		Quadratic ideal observation and recurrent preprocessing in perceptual learning.
		A familiarity-based learning procedure for the establishment of place fields in area CA3 of the rat hippocampus.
	www.gatsby.ucl.ac.uk /~dayan/papers (1087 words)

	Unsupervised Learning Using MML - Oliver, Wallace (ResearchIndex) (Site not responding. Last check: )
		An important part of the unsupervised learning problem is determining the number of constituent groups (components or classes) which best describes some data.
		We apply the Minimum Message Length (MML) criterion to the unsupervised learning problem, modifying an earlier such MML application.
		J.J. Oliver, Baxter R.A., and Wallace C.S. Unsupervised Learning using MML.
	citeseer.ist.psu.edu /88043.html (644 words)

	[No title]
		The problem of learning is understood as development of the strategy of selective data collection, their further proper organization and development of the applicable model for extraction of reactive rules and formation of deliberative plans.
		As a byproduct of the learning processes, the data end up to be organized in a multigranular (multiscale) system of "world representation" which allows for effective interpretation and direct use for computing multiresolutional planning and control sequences.
		The space is learned in advance by multiple testing, and its representation is based upon knowing that the distance, velocity and time are linked by a simple expression which is sufficient for obtaining computationally the theoretically correct solution with an error accepted to be admissible.
	dimacs.rutgers.edu /Workshops/Classification/doc/MEYSTEL.DOC (1974 words)

	Workshop in Bonn (Site not responding. Last check: )
		Unsupervised learning is currently one of the most active research areas of Neural Computation.
		Unsupervised learning algorithms will become a key technology to automate the detection of such implicit structure or, on a more modest scale, to assist
		unsupervised learning techniques, but were most currently employed approaches are still in their infancy and are using rather ad-hoc techniques instead of
	www-dbv.cs.uni-bonn.de /dagstuhl (713 words)

	Amazon.com: Unsupervised Learning: Foundations of Neural Computation (Computational Neuroscience): Books: Geoffrey ... (Site not responding. Last check: )
		This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher.
		The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs.
		Forthmore this book is covering information on unsupervised learning and covering topics like local synaptic learning and hebbian learnig.
	www.amazon.com /exec/obidos/tg/detail/-/026258168X?v=glance (501 words)

	Phil/Psych 446, Week 8 (Site not responding. Last check: )
		The strength of a connection from a unit acting as a threshold bias is made according to calculation 4 or 5 except that the unit's activation = 1.
		Hebbian learning, in which the weight between two units is increased if the two units are simultaneously active, is more neurologically realistic.
		There are good introductory articles on neural networks, supervised learning, unsupervised learning, and recurrent networks in the MIT Encyclopedia of Cognitive Science, available in the Porter library reference section.
	cogsci.uwaterloo.ca /courses/Phil446/Phil446.week8.html (1772 words)

	Machine Learning (Theory) » Unsupervised (Site not responding. Last check: )
		One of the common trends in machine learning has been an emphasis on the use of unlabeled data.
		A less extreme version was the DARPA grand challenge winner where the output of a laser range finder was used to form a road-or-not predictor for a camera image.
		These automated labeling techniques transform an unsupervised learning problem into a supervised learning problem, which has huge implications: we understand supervised learning much better and can bring to bear a host of techniques.
	hunch.net /index.php?cat=10 (1291 words)

	Stanford NLP Group
		Humans are able to acquire linguistic knowledge in a more or less unsupervised manner.
		Although machines lack the contextual situation of a human learner, as well as whatever innate knowledge humans might have, much of the structure of natural language is distributionally detectable.
		The more linguistic structure that can be automatically learned, the less need there is for large marked-up corpora, which are costly in both time and expertise.
	nlp.stanford.edu /~danklein/project-induction.shtml (312 words)

	Plato - Plato Learning Inc
		Dedicated to enhancing the quality of life for all individuals with learning disabilities and their families, to alleviating the restricting effects of learning disabilities, and to supporting endeavors to determine the causes of learning disabilities.
		LDI is a transdisciplinary networked learning community devoted to excellence in the development and study of learning. The collective responsibility for the ecology of the learning environment, and each individual's role in its participatory management, is among LDI's essential concerns.
		A central focus for learning technology research on: design innovation using multimedia and the Web; the development of e-learning environments; theory and practice for evaluation; and the theoretical basis of learning technology.
	www.platon.org /platolearninginc (1537 words)

	Unsupervised learning of natural languages -- Solan et al. 102 (33): 11629 -- Proceedings of the National Academy of ...
		Unsupervised learning of natural languages -- Solan et al.
		This paper was submitted directly (Track II) to the PNAS office.
		In testing a learned grammar G for strong generativity, the
	www.pnas.org /cgi/content/abstract/102/33/11629 (440 words)

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