Search results for "Inductive reasoning"

showing 10 items of 54 documents

On the power of inductive inference from good examples

1993

Abstract The usual information in inductive inference available for the purposes of identifying an unknown recursive function f is the set of all input/output examples (x,f(x)),n eN. In contrast to this approach we show that it is considerably more powerful to work with finite sets of “good” examples even when these good examples are required to be effectively computable. The influence of the underlying numberings, with respect to which the identification has to be realized, to the capabilities of inference from good examples is also investigated. It turns out that nonstandard numberings can be much more powerful than Godel numberings.

Teaching dimensionSet (abstract data type)Identification (information)General Computer ScienceInferenceContrast (statistics)Inductive reasoningFinite setAlgorithmPower (physics)MathematicsTheoretical Computer ScienceComputer Science(all)Theoretical Computer Science
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On the impact of forgetting on learning machines

1995

People tend not to have perfect memories when it comes to learning, or to anything else for that matter. Most formal studies of learning, however, assume a perfect memory. Some approaches have restricted the number of items that could be retained. We introduce a complexity theoretic accounting of memory utilization by learning machines. In our new model, memory is measured in bits as a function of the size of the input. There is a hierarchy of learnability based on increasing memory allotment. The lower bound results are proved using an unusual combination of pumping and mutual recursion theorem arguments. For technical reasons, it was necessary to consider two types of memory : long and sh…

Theoretical computer scienceActive learning (machine learning)Computer scienceSemi-supervised learningMutual recursionArtificial IntelligenceInstance-based learningHierarchyForgettingKolmogorov complexitybusiness.industryLearnabilityAlgorithmic learning theoryOnline machine learningInductive reasoningPumping lemma for regular languagesTerm (time)Computational learning theoryHardware and ArchitectureControl and Systems EngineeringArtificial intelligenceSequence learningbusinessSoftwareCognitive psychologyInformation SystemsJournal of the ACM
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An inductive inference approach to classification

1992

In this paper, we introduce a formal framework for investigating the relationship of inductive inference and the task of classification. We give the first results on the relationship between functions that can be identified in the limit and functions that can be acquired from unclassified objects only. Moreover, we present results on the complexity of classification functions and the preconditions necessary in order to allow the computation of such functions.

Theoretical computer scienceComputer scienceOrder (business)ComputationLimit (mathematics)Inductive reasoningTask (project management)
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On the Influence of Technology on Learning Processes

2014

Probabilistic computations and frequency computations were invented for the same purpose, namely, to study possible advantages of technology involving random choices. Recently several authors have discovered close relationships of these generalizations of deterministic computations to computations taking advice. Various forms of computation taking advice were studied by Karp and Lipton [1], Damm and Holzer [2], and Freivalds [3]. In the present paper, we apply the nonconstructive, probabilistic, and frequency methods to an inductive inference paradigm originally due to Gold [4] and investigate their impact on the resulting learning models. Several trade-offs with respect to the resulting l…

Theoretical computer scienceHardware and ArchitectureComputer scienceLearnabilityComputationProbabilistic logicLearning modelsInductive reasoningAdvice (complexity)SoftwareTheoretical Computer ScienceParallel Processing Letters
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Incorporating hypothetical knowledge into the process of inductive synthesis

1996

The problem of inductive inference of functions from hypothetical knowledge is investigated in this paper. This type of inductive inference could be regarded as a generalization of synthesis from examples that can be directed not only by input/output examples but also by knowledge of, e. g., functional description's syntactic structure or assumptions about the process of function evaluation. We show that synthesis of this kind is possible by efficiently enumerating the hypothesis space and illustrate it with several examples.

Theoretical computer scienceInductive biasGeneralizationComputer scienceProcess (engineering)business.industrymedia_common.quotation_subjectSpace (commercial competition)Type (model theory)Inductive reasoningMachine learningcomputer.software_genreFunctional descriptionArtificial intelligenceFunction (engineering)businesscomputermedia_common
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Learning small programs with additional information

1997

This paper was inspired by [FBW 94]. An arbitrary upper bound on the size of some program for the target function suffices for the learning of some program for this function. In [FBW 94] it was discovered that if “learning” is understood as “identification in the limit,” then in some programming languages it is possible to learn a program of size not exceeding the bound, while in some other programming languages this is not possible.

Theoretical computer sciencebusiness.industryComputer sciencemedia_common.quotation_subjectInductive reasoningMachine learningcomputer.software_genreUpper and lower boundsIdentification (information)Recursive functionsArtificial intelligenceLimit (mathematics)businessFunction (engineering)computermedia_common
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The power of procrastination in inductive inference: How it depends on used ordinal notations

1995

We consider inductive inference with procrastination. Usually it is defined using constructive ordinals. For constructive ordinals there exist many different systems of notations. In this paper we study how the power of inductive inference depends on used system of notations.

Theoretical computer sciencebusiness.industrymedia_common.quotation_subjectProcrastinationInductive reasoningMachine learningcomputer.software_genreNotationConstructivePower (physics)Mathematics::LogicArtificial intelligencebusinesscomputermedia_commonMathematics
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Inductive inference of recursive functions: Qualitative theory

2005

This survey contains both old and very recent results in non-quantitative aspects of inductive inference of total recursive functions. The survey is not complete. The paper was written to stress some of the main results in selected directions of research performed at the University of Latvia rather than to exhaust all of the obtained results. We concentrated on the more explored areas such as the inference of indices in non-Goedel computable numberings, the inference of minimal Goedel numbers, and the specifics of inference of minimal indices in Kolmogorov numberings.

Turing machinesymbols.namesakeTheoretical computer scienceInductive biasInductive probabilitysymbolsRecursive functionsInferenceInductive reasoningGödel's incompleteness theoremsQualitative theoryMathematics
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Towards A Twitter Observatory: A Multi-Paradigm Framework For Collecting, Storing And Analysing Tweets

2016

International audience; In this article we show how a multi-paradigm framework can fulfil the requirements of tweets analysis and reduce the waiting time for researchers that use computational resources and storage systems to support large-scale data analysis. The originality of our approach is to combine concerns about data harvesting, data storage, data analysis and data visualisation into a framework that supports inductive reasoning in multidisciplinary scientific research. Our main contribution is a polyglot storage system with a generic data model to support logical data independence and a set of tools that can provide a suitable solution for mixing different types of algorithms in or…

[ INFO.INFO-IR ] Computer Science [cs]/Information Retrieval [cs.IR][ INFO ] Computer Science [cs]Computer scienceknowledge discovery02 engineering and technology[INFO] Computer Science [cs][INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI]Data modelingmassive datasetsopen source softwareData visualization[ INFO.INFO-IT ] Computer Science [cs]/Information Theory [cs.IT]polyglot storage020204 information systems0202 electrical engineering electronic engineering information engineering[INFO]Computer Science [cs]Twitter analysis . SystemsComputingMilieux_MISCELLANEOUS[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB]business.industryPolyglotInductive reasoningData science[SPI.TRON] Engineering Sciences [physics]/ElectronicsData independence[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/ElectronicsData model[INFO.INFO-IT]Computer Science [cs]/Information Theory [cs.IT][INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR]020201 artificial intelligence & image processing[INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR][INFO.INFO-IT] Computer Science [cs]/Information Theory [cs.IT]Data architecturebusinessSoftware architecture
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Pattern languages with and without erasing

1994

The paper deals with the problems related to finding a pattern common to all words in a given set. We restrict our attention to patterns expressible by the use of variables ranging over words. Two essentially different cases result, depending on whether or not the empty word belongs to the range. We investigate equivalence and inclusion problems, patterns descriptive for a set, as well as some complexity issues. The inclusion problem between two pattern languages turns out to be of fundamental theoretical importance because many problems in the classical combinatorics of words can be reduced to it.

business.industryApplied MathematicsInferenceComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)Inductive reasoningcomputer.software_genreComputer Science ApplicationsPhilosophy of languageComputational Theory and MathematicsrestrictFormal languageArtificial intelligenceEquivalence (formal languages)ArithmeticbusinesscomputerComputer Science::Formal Languages and Automata TheoryNatural language processingMathematicsInternational Journal of Computer Mathematics
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