Search results for "Machine"

showing 10 items of 2592 documents

Multi-Dimensional motivic pattern extraction founded on adaptive redundancy filtering

2005

Abstract We present a computational model for discovering repeated patterns in symbolic representations of monodic music. Patterns are discovered through an incremental adaptive identification along a multi-dimensional parametric space. The difficulties of pattern discovery mainly come from combinatorial redundancies, that our model is able to control efficiently. A specificity relation is defined between pattern descriptions, unifying suffix and inclusion relations and enabling a filtering of redundant descriptions. Combinatorial proliferation caused by successive repetitions of patterns is managed using cyclic patterns. The modelling of these redundancy control mechanisms enables an autom…

Theoretical computer scienceVisual Arts and Performing ArtsRelation (database)Space (commercial competition)050105 experimental psychology060404 music[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[STAT.ML]Statistics [stat]/Machine Learning [stat.ML][INFO.INFO-FL]Computer Science [cs]/Formal Languages and Automata Theory [cs.FL]Redundancy (engineering)0501 psychology and cognitive sciencesControl (linguistics)MathematicsParametric statistics[INFO.INFO-PL]Computer Science [cs]/Programming Languages [cs.PL][SHS.MUSIQ]Humanities and Social Sciences/Musicology and performing artsbusiness.industry05 social sciences06 humanities and the artsAutomation[INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD]Multi dimensionalNASuffixbusiness0604 artsMusic
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Robustness and Randomness

2008

The study of robustness problems for computational geometry algorithms is a topic that has been subject to intensive research efforts from both computer science and mathematics communities. Robustness problems are caused by the lack of precision in computations involving floating-point instead of real numbers. This paper reviews methods dealing with robustness and inaccuracy problems. It discusses approaches based on exact arithmetic, interval arithmetic and probabilistic methods. The paper investigates the possibility to use randomness at certain levels of reasoning to make geometric constructions more robust.

Theoretical computer sciencebusiness.industryComputation020207 software engineering0102 computer and information sciences02 engineering and technologyMachine learningcomputer.software_genre01 natural sciencesInterval arithmeticProbabilistic method010201 computation theory & mathematicsRobustness (computer science)0202 electrical engineering electronic engineering information engineeringArtificial intelligencebusinesscomputerRandomnessMathematicsReal number
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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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Descriptional and Computational Complexity of the Circuit Representation of Finite Automata

2018

In this paper we continue to investigate the complexity of the circuit representation of DFA—BC-complexity. We compare it with nondeterministic state complexity, obtain upper and lower bounds which differ only by a factor of 4 for a Binary input alphabet. Also we prove that many simple operations (determining if a state is reachable or if an automaton is minimal) are PSPACE-complete for DFA given in circuit representation.

TheoryofComputation_COMPUTATIONBYABSTRACTDEVICESFinite-state machineTheoretical computer scienceComputational complexity theoryComputer science020208 electrical & electronic engineering020206 networking & telecommunications02 engineering and technologyUpper and lower boundsAutomatonNondeterministic algorithmTheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGESSimple (abstract algebra)0202 electrical engineering electronic engineering information engineeringState (computer science)Representation (mathematics)Computer Science::Formal Languages and Automata Theory
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An Approximate Determinization Algorithm for Weighted Finite-State Automata

2001

Nondeterministic weighted finite-state automata are a key abstraction in automatic speech recognition systems. The efficiency of automatic speech recognition depends directly on the sizes of these automata and the degree of nondeterminism present, so recent research has studied ways to determinize and minimize them, using analogues of classical automata determinization and minimization. Although, as we describe here, determinization can in the worst case cause poly-exponential blowup in the number of states of a weighted finite-state automaton, in practice it is remarkably successful. In extensive experiments in automatic speech recognition systems, deterministic weighted finite-state autom…

TheoryofComputation_COMPUTATIONBYABSTRACTDEVICESFinite-state machineTheoretical computer scienceGeneral Computer ScienceComputer scienceApplied MathematicsComputer Science ApplicationsAutomatonNondeterministic algorithmNondeterministic finite automaton with ε-movesComputer Science::SoundDeterministic automatonTheory of computationStandard testMinificationAlgorithmComputer Science::Formal Languages and Automata TheoryAlgorithmica
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Quantum versus Probabilistic One-Way Finite Automata with Counter

2001

The paper adds the one-counter one-way finite automaton [6] to the list of classical computing devices having quantum counterparts more powerful in some cases. Specifically, two languages are considered, the first is not recognizable by deterministic one-counter one-way finite automata, the second is not recognizable with bounded error by probabilistic one-counter one-way finite automata, but each recognizable with bounded error by a quantum one-counter one-way finite automaton. This result contrasts the case of one-way finite automata without counter, where it is known [5] that the quantum device is actually less powerful than its classical counterpart.

TheoryofComputation_COMPUTATIONBYABSTRACTDEVICESNested wordComputer scienceTimed automatonBüchi automatonω-automatonNondeterministic finite automaton with ε-movesTuring machinesymbols.namesakeDFA minimizationDeterministic automatonContinuous spatial automatonQuantum finite automataDeterministic system (philosophy)Two-way deterministic finite automatonNondeterministic finite automatonDiscrete mathematicsFinite-state machineQuantum dot cellular automatonNonlinear Sciences::Cellular Automata and Lattice GasesMobile automatonTheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGESDeterministic finite automatonProbabilistic automatonsymbolsAutomata theoryComputer Science::Formal Languages and Automata TheoryQuantum cellular automaton
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Multiple Usage of Random Bits in Finite Automata

2012

Finite automata with random bits written on a separate 2-way readable tape can recognize languages not recognizable by probabilistic finite automata. This shows that repeated reading of random bits by finite automata can have big advantages over one-time reading of random bits.

TheoryofComputation_COMPUTATIONBYABSTRACTDEVICESNested wordFinite-state machineTheoretical computer scienceKolmogorov complexityComputer scienceω-automatonNonlinear Sciences::Cellular Automata and Lattice GasesBit fieldTuring machinesymbols.namesakeTheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGESsymbolsQuantum finite automataAutomata theoryArithmeticComputer Science::DatabasesComputer Science::Formal Languages and Automata Theory
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Tally languages accepted by Monte Carlo pushdown automata

1997

Rather often difficult (and sometimes even undecidable) problems become easily decidable for tally languages, i.e. for languages in a single-letter alphabet. For instance, the class of languages recognizable by 1-way nondeterministic pushdown automata equals the class of the context-free languages, but the class of the tally languages recognizable by 1-way nondeterministic pushdown automata, contains only regular languages [LP81]. We prove that languages over one-letter alphabet accepted by randomized one-way 1-tape Monte Carlo pushdown automata are regular. However Monte Carlo pushdown automata can be much more concise than deterministic 1-way finite state automata.

TheoryofComputation_COMPUTATIONBYABSTRACTDEVICESNested wordTheoretical computer scienceComputational complexity theoryComputer scienceDeterministic pushdown automatonTuring machinesymbols.namesakeRegular languageComputer Science::Logic in Computer ScienceQuantum finite automataNondeterministic finite automatonDiscrete mathematicsFinite-state machineDeterministic context-free languageComputabilityDeterministic context-free grammarContext-free languagePushdown automatonAbstract family of languagesComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)Cone (formal languages)Embedded pushdown automatonUndecidable problemNondeterministic algorithmTheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGESDeterministic finite automatonsymbolsComputer Science::Programming LanguagesAlphabetComputer Science::Formal Languages and Automata Theory
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The computational power of continuous time neural networks

1997

We investigate the computational power of continuous-time neural networks with Hopfield-type units. We prove that polynomial-size networks with saturated-linear response functions are at least as powerful as polynomially space-bounded Turing machines.

TheoryofComputation_COMPUTATIONBYABSTRACTDEVICESQuantitative Biology::Neurons and CognitionComputational complexity theoryArtificial neural networkComputer sciencebusiness.industryComputer Science::Neural and Evolutionary ComputationNSPACEComputational resourcePower (physics)Turing machinesymbols.namesakeCellular neural networksymbolsArtificial intelligenceTypes of artificial neural networksbusiness
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