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	Inductive bias - Wikipedia, the free encyclopedia
		Informally speaking, the inductive bias of a machine learning algorithm refers to additional assumptions, that the learner will use to predict correct outputs for situations that have not been encountered so far.
		A classical example of an inductive bias is Occam's Razor, assuming that the simplest consistent hypothesis about the target function is actually the best.
		Here, the inductive bias is a logical formula that, together with the training data, logically entails the hypothesis generated by the learner.
	en.wikipedia.org /wiki/Inductive_bias (267 words)

	Inductive bias -- Facts, Info, and Encyclopedia article (Site not responding. Last check: 2007-10-21)
		Informally speaking, the inductive bias of a (additional info and facts about machine learning) machine learning (A precise rule (or set of rules) specifying how to solve some problem) algorithm refers to additional assumptions, that the learner will use to predict correct outputs for situations that have not been encountered so far.
		A classical example of an inductive bias is (additional info and facts about Occam's Razor) Occam's Razor, assuming that the simplest consistent hypothesis about the target function is actually the best.
		Approaches to a more formal definition of inductive bias are based on mathematical (The branch of philosophy that analyzes inference) logic.
	www.absoluteastronomy.com /encyclopedia/i/in/inductive_bias.htm (305 words)

	[No title] (Site not responding. Last check: 2007-10-21)
		A first bias element having an inductive impedance characteristic is coupled to the first switching element and a second bias element having an inductive impedance characteristic is coupled to the second switching element.
		The loop inductive bias element LQ1A therefore reduces the voltage that appears across the remainder of the circuit loop elements, e.
		The circuit according to claim 23, wherein the first bias element has a polarity with respect to a polarity of the resonant inductive element such that the first bias element biases the first switching element to resist avalanche current flow when the first diode is conductive.
	www.wipo.int /cgi-pct/guest/getbykey5?KEY=99/57801.991111&ELEMENT_SET=DECL (4180 words)

	Machine Learning/Inductive Inference
		The inductive bias of the decision tree ID3 algorithm is a preference for certain hypotheses over others (e.g., for shorter hypotheses), with no hard restriction on the hypotheses that can be eventually enumerated.
		In contrast, the bias of the version space candidate-elimination algorithm is in the form of a categorical restriction on the set of hypotheses considered.
		A restriction bias that strictly limits the set of potential hypotheses is generally less desirable because it introduces the possibility of excluding the unknown target function altogether.
	www.cs.uregina.ca /~dbd/cs831/notes/ml/2_inference.html (449 words)

	[No title] (Site not responding. Last check: 2007-10-21)
		Inductive concept formation is, trivially, the process of partitioning a set of instances into subsets, whose compact descriptions represent the desired target concepts.
		Mitchell (1980) first defined inductive bias as anything which causes an inductive algorithm to choose one concept over another aside from strict consistency with the training set.
		Inductive bias can be expressed as feature abstraction, restrictions on the language of concept expression, search operators, feature construction operators, etc. Typically, biases are implicit in the design and implementation of the induction algorithm.
	www.ics.uci.edu /~mlearn/databases/dgp-2/DGP-2.names (3761 words)

	USS Clueless - Notes on press bias
		bias in favor of the vivid, the surprising, the shocking (misleading vividness)
		Inductive decisions are inherently subjective, but it cannot be done any other way.
		Given that there are a small number of such filtration layers, with a long term tendency of each layer to learn and adapt to what the next layer tends to approve of, then even with the best intentions there will always be considerable distortion in reportage, especially in terms of what is ultimately considered "newsworthy".
	denbeste.nu /cd_log_entries/2004/05/Notesonpressbias.shtml (3704 words)

	Overfitting - TheBestLinks.com - Algorithm, Statistics, Occam's razor, Inductive bias, ...
		Overfitting, Algorithm, Statistics, Occam's razor, Inductive bias, Early...
		The learner is assumed to reach a state where it will also be able to predict the correct output for other examples, thus generalizing to situations not presented during training (based on its inductive bias).
		However, especially in cases where learning was performed too long or where training examples are rare, the learner may adjust to very specific random features of the training data, that have no causal relation to the target function.
	www.thebestlinks.com /Overfitting.html (289 words)

	Class 6: Blind and Heuristic Search
		Inductive inference arrives at general conclusions by examining particular examples.
		For instance, when robot finds out that several encountered tall and wide objects are immovable, it may conclude that all tall and wide objects are immovable.
		Such bias provides a learning program for choosing among possible representations of f.
	cs.gmu.edu /~dkaznach/cs480-2004-02/11/LearningVersionSpaces.htm (1284 words)

	Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement
		To evaluate long-term effects of bias shifts setting the stage for later bias shifts we use the ``success-story algorithm'' (SSA).
		It uses backtracking to undo those bias shifts that have not been empirically observed to trigger long-term reward accelerations (measured up until the current SSA call).
		Bias shifts that survive SSA represent a lifelong success history.
	www.idsia.ch /~juergen/mljssalevin (209 words)

	Introduction / Overview
		Inductive transfer of knowledge from one task solution to the next (e.g., Caruana et al.
		In case of policy changes or bias shifts, information necessary to restore the old policy is pushed on a stack.
		For instance, certain bias shifts may have been too specifically tailored to previous tasks (``overfitting'') and may be harmful for future inductive transfer.
	www.idsia.ch /~juergen/mljssalevin/node1.html (786 words)

	Inductive Learning in Deductive Databases
		Most current applications of inductive learning in databases take place in the context of a single extensional relation.
		The authors place inductive learning in the context of a set of relations defined either extensionally or intentionally in the framework of deductive databases.
		LINUS, an inductive logic programming system that induces virtual relations from example positive and negative tuples and already defined relations in a deductive database, is presented.
	csdl2.computer.org /persagen/DLAbsToc.jsp?resourcePath=/dl/trans/tk/&toc=comp/trans/tk/1993/06/k6toc.xml&DOI=10.1109/69.250076 (739 words)

	[No title]
		Typically such bias is supplied by hand through the skill and insights of experts.
		In this paper a model for automatically learning bias is investigated.
		The central assumption of the model is that the learner is embedded within an environment of related learning tasks.
	www.uni-koblenz-landau.de:81 /~fruit/PAPERS/learning.inc-bib.html (810 words)

	Technical Abstracts 1995
		Abstract: This paper extends the currently accepted model of inductive bias by identifying six categories of bias and separates inductive bias from the policy for its selection (the inductive policy).
		We analyze existing "bias selection" systems, examining the similarities and differences in their inductive policies, and identify three techniques useful for building inductive policies.
		We illustrate this exploration with a problem to which we have applied the RL induction program, the problem of predicting whether or not a chemical is a likely carcinogen.
	www.cs.pitt.edu /research/publications/trabstracts95.html (4286 words)

	Research grant: Inductive Logic Programming
		Inductive logic programming (ILP) is the intersection of inductive learning and logic programming.
		The main long term technical goal of the ILP project is to update the techniques of the classical empirical learning paradigm to a logic programming framework.
		declarative bias: the exploration of methods and formalisms to explicitly and declaratively represent the bias of inductive logic learners
	web.comlab.ox.ac.uk /oucl/research/grants/hw.html (438 words)

	Inductive Logic Programming - Muggleton (ResearchIndex)
		While inheriting various positive characteristics of the parent subjects of Logic Programming and Machine Learning, it is hoped that the new area will overcome many of the limitations of its forebears.
		The background to present developments within this area is discussed and various goals and aspirations for the increasing body of researchers are identified.
		Inductive Logic Programming needs to be based on sound principles...
	citeseer.ist.psu.edu /muggleton92inductive.html (768 words)

	Citations: Quantifying inductive bias: AI learning algorithms and Valiant's learning framework - Haussler ...
		Two well known inductive learning systems that use this approach are id3 [Qui86] which uses a greedy technique to reduce the expected entropy of a decision tree, and backprop....
		Haussler, D., "Quantifying Inductive Bias: AI Learning Algorithms and Valiant's Model", Artificial Intelligence, 36, 1988, pp.
		Haussler, `Quantifying inductive bias: AI learning algorithms and Valiant's learning framework', J. Artificial Intelligence, 36, 177-221, 1988.
	citeseer.ist.psu.edu /context/792/0 (1292 words)

	M.R. Bauer Foundation at Brandeis
		Because of the need to carefully specify the input form and model class, every ML method converges before achieving the kind of autonomous learning necessary for embedding into agents who face a novel and changing world.
		There is a new opportunity for breaking through this inductive bias paradox -- "Co-Evolution" -- which involves adaptive learning agents within adaptive environments.
		Our research is focused on the principles by which systems which can undergo a sustained growth in their abilities, rather than on systems which succeed at a given task because of the skill of the programmer developing the inductive bias in the learning algorithm or in the careful representation of the learning environment.
	www.bio.brandeis.edu /bauer/1997/pollack.html (671 words)

	(WO 99/57801) INVERTER CIRCUIT WITH AVALANCHE CURRENT PREVENTION (Site not responding. Last check: 2007-10-21)
		(57) An electronic circuit includes first and second inductive bias elements coupled to respective first and second switching elements for preventing avalanche current flow through the switching elements.
		A first inductive bias element is coupled to the first switching element and a second inductive bias element is coupled to the second switching element.
		The first and second bias elements are inductively coupled with a resonant inductive element for biasing the respective first and second switching elements against avalanche current flow.
	wipo.int /cgi-pct/guest/getbykey5?KEY=99/57801.991111&ELEMENT_SET=DECL (163 words)

	CS 527A: Homework 2
		The inductive bias of FindS is to output a most specific hypothesis in the version space.
		In class we briefly argued that given the hypothesis space of conjunctions there is always a single element of S (the most specific boundary of the version space) and FindS simply computes this element.
		You are welcome to select any inductive bias that you would like.
	www.cs.wustl.edu /~sg/CS527_SP02/hw2.html (1562 words)

	Machine Learning/Inductive Inference/Decision Trees/Construction
		ID3's inductive bias is based on the ordering of hypotheses by its search strategy (ie.
		Approximate inductive bias of ID3: shorter trees are preferred over larger trees.
		A closer approximation to the inductive bias of ID3: shorter trees are preferred over longer trees.
	www.cs.uregina.ca /~dbd/cs831/notes/ml/dtrees/4_dtrees2.html (893 words)

	ILPNET books
		The book is an introduction to inductive logic programming (ILP), a research area at the intersection of inductive machine learning and logic programming.
		This field aims at a formal framework and practical algorithms for inductively learning relational descriptions in the form of logic programs.
		ILP is of interest to inductive machine learning researchers as it significantly extends the usual attribute-value respresentation and consequently enlarges the scope of machine learning applications; it is also of interest to logic programming researchers as it extends the basically deductive framework of logic programming with the use of induction.
	www-ai.ijs.si /ilpnet/books.html (642 words)

	Publications of the Machine Learning Group
		De Raedt and M. Bruynooghe, "Indirect relevance and bias in inductive concept-learning," Knowledge Acquisition, vol.
		De Raedt and M. Bruynooghe, "On explanation and bias in inductive concept-learning," in Proceedings of the 3rd European Knowledge Acquisition for knowledge based systems Workshop, pp.
		De Raedt and M. Bruynooghe, "On Explanation and Bias in Inductive Concept-Learning," Tech.
	www.cs.kuleuven.ac.be /~ml/mlrg/publications-E.shtml (1843 words)

	Change of Representation and Inductive Bias (Site not responding. Last check: 2007-10-21)
		Abstract: Proceedings of the First International Workshop on Change of Representation and Inductive Bias, held June 8-10, 1988 in Tarrytown, N.Y. Keywords: book review, ai, knowledge representation, artificial intelligence, machine learning, expert systems, computer science, information theory.
		This book is being considered for further review, but we do not yet have a copy in hand.
		Title: Change of representation and inductive bias / edited by D. Paul Benjamin.
	www.weyrich.com /book_reviews/representation_inductive.html (137 words)

	Mike R. Jay on Leadership: Leadership is full of Cul de Sacs?
		In the book, he essentially claims each person is a computation resulting from a program of learning called DNA, which has been developed by evolution over billions of years.
		One of the points he makes that I want to use here is the idea that as a computation, we have certain limits, or what the author calls inductive bias, as a result of the need to bound reality.
		An approach is more than likely a representation of your own inductive bias; accept that, be open to others.
	generati.typepad.com /mrj/2005/04/leadership_is_f.html (909 words)

	Cogprints - How to shift bias: Lessons from the Baldwin effect (Site not responding. Last check: 2007-10-21)
		An inductive learning algorithm takes a set of data as input and generates a hypothesis as output.
		Algorithms that shift bias in this manner are not as well understood as classical algorithms.
		The main lesson is that it appears that a good strategy for shift of bias in a learning algorithm is to begin with a weak bias and gradually shift to a strong bias.
	cogprints.org /1818 (904 words)

	Decision Tree Learning
		It is one of the most widely used and practical methods for inductive inference.
		Preference bias (relative to restriction bias as in the VS approach)
		The training error is statistically smaller than the test error for a given hypothesis.
	www.cise.ufl.edu /~fu/Lecture/Learn/decision-fu.html (294 words)

	Index of all on-line articles of 1992
		declarative and dynamically adjustable language bias in concept learning.
		Inductive Logic Programming, Report ICOT TM-1182, page 18, 1992.
		Inductive Logic Programming, Report ICOT TM-1182, pages 19--39, 1992.
	www.cs.bris.ac.uk /~ILPnet2/Tools/Reports/references-1992.html (1248 words)

	Cogprints - Technical note: Bias and the quantification of stability (Site not responding. Last check: 2007-10-21)
		Research on bias in machine learning algorithms has generally been concerned with the impact of bias on predictive accuracy.
		We believe that there are other factors that should also play a role in the evaluation of bias.
		This paper introduces a method for quantifying stability, based on a measure of the agreement between concepts.
	cogprints.org /1819 (332 words)

	A Model of Inductive Bias Learning - Baxter (ResearchIndex)
		The central assumption of the model is that the learner is embedded...
		Thus, we have identi ed a particular mechanism for extracting useful bias from related problems (see e.g.
		38 Adapting bias by gradient descent: An incremental version of..
	citeseer.ist.psu.edu /339127.html (913 words)

	[No title]
		.sh 1 "Inductive Learning from Examples".pp Acquiring general concept descriptions by examining the similarities and differences between teacher-supplied examples and counter-examples.
		.sp.5 "A Theory and Methodology of Inductive Inference," pp.
		Using the size of the hypothesis space to quantify bias.
	www.cs.utexas.edu /ftp/pub/mooney/ml-course/syllabus (378 words)

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