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Learning to predict non-deterministically generated strings
1991
Machine Learning
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In this article we present an algorithm that learns to predict non-deterministically generated strings. ...
The problem of learning to predict non-deterministically generated strings was raised by Dietterich and Michalski (1986) . ...
Introduction In order to illustrate what we mean by learning non-deterministically generated strings we consider the case of language acquisition. ...
doi:10.1007/bf00058927
fatcat:p2piauq2lraqhfxxetda3adjgm
PAutomaC: a probabilistic automata and hidden Markov models learning competition
2013
Machine Learning
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Approximating distributions over strings is a hard learning problem. ...
Both artificial data and real data were presented and contestants were to try to estimate the probabilities of strings. ...
Acknowledgements We are very thankful to the members of the scientific committee for their help in designing this competition. ...
doi:10.1007/s10994-013-5409-9
fatcat:ey3ghvxqxzbevmpzxmv5tytvay
Rapid on-line temporal sequence prediction by an adaptive agent
2005
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems - AAMAS '05
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We consider the case of near-future event prediction by an online learning agent operating in a non-stationary environment. ...
The method compares well against Markov-chain predictions, and adapts faster than learned Markov-chain models to changes in the underlying distribution of events. ...
This method yielded non-stationary strings in which highly deterministic sections from one process were followed by highly deterministic sections from a different process. ...
doi:10.1145/1082473.1082484
dblp:conf/atal/JensenBGS05
fatcat:inib7hsksbeepp2rt2jhpxnb4u
Results of the PAutomaC Probabilistic Automaton Learning Competition
2012
Journal of machine learning research
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Approximating distributions over strings is a hard learning problem. ...
Both artificial data and real data were proposed and contestants were to try to estimate the probabilities of test strings. ...
Acknowledgments We are very thankful to the members of the scientific committee for their help in designing this competition. ...
dblp:journals/jmlr/VerwerEH12
fatcat:orcojpoilveyliyoaeteagyo6e
Predictability of imitative learning trajectories
2019
Journal of Statistical Mechanics: Theory and Experiment
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2018 pre-print arXiv:1807.04862v2 fatcat:u3pvehubbrhh5nkhsmxw7uyh3eOther Versions
The learning trajectories become more deterministic, in the sense that there are fewer distinct trajectories and those trajectories are more similar to each other, with increasing population size and imitation ...
We assess the degree to which the starting and ending points determine the learning trajectories using two measures, namely, the predictability that yields the probability that two randomly chosen trajectories ...
In the evolutionary algorithms, however, there is no such a natural choice: the fittest string at a given generation is more likely to contribute offsprings to the the next generation but does not have ...
doi:10.1088/1742-5468/aaf634
fatcat:6py7fcwbzja7dni4o26bor4lhe
Ten Open Problems in Grammatical Inference
[chapter]
2006
Lecture Notes in Computer Science
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They cover the areas of polynomial learning models, learning from ordered alphabets, learning deterministic Pomdps, learning negotiation processes, learning from context-free background knowledge. ...
In all cases, problems are theoretically oriented but correspond to practical questions. ...
Acknowledgements Thanks to Jose Oncina for different discussions that led to several definitions and problems from sections 4 and 6. ...
doi:10.1007/11872436_4
fatcat:5snm4lpumbhw5e7ffh5vwxzhum
Universal Learning Theory
[article]
2011
arXiv
pre-print
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It explains the spirit of universal learning, but necessarily glosses over technical subtleties. ...
This encyclopedic article gives a mini-introduction into the theory of universal learning, founded by Ray Solomonoff in the 1960s and significantly developed and extended in the last decade. ...
One solution is to take into account our (whole) scientific prior knowledge z [Hut06] , and predicting the now long string zx leads to good (less sensitive to "reasonable" U) predictions. ...
arXiv:1102.2467v1
fatcat:m6voura42jcmvknemk7cbb7qf4
Strongly Unambiguous Büchi Automata Are Polynomially Predictable With Membership Queries
2020
Annual Conference for Computer Science Logic
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In contrast, under plausible cryptographic assumptions, non-deterministic Büchi automata are not polynomially predictable with membership queries. ...
using a non-deterministic Büchi automaton (Theorem 1), it is polynomially predictable with membership queries when the target language is represented using a strongly unambiguous Büchi automaton (Corollary ...
Finally, when A requests the test word to predict, we request the test word to predict, and receive a string x ∈ Σ * , chosen according to D. ...
doi:10.4230/lipics.csl.2020.8
dblp:conf/csl/AngluinAF20
fatcat:gqrbomby4jcytg57umnctwqdzi
Pseudo-Derandomizing Learning and Approximation
2018
International Workshop on Approximation Algorithms for Combinatorial Optimization
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Our goal is to simulate known randomized algorithms in these settings by pseudo-deterministic algorithms in a generic fashion -a goal we succinctly term pseudo-derandomization. Learning. ...
In particular, this suggests a new approach to constructing hitting set generators against AC 0 [p] circuits by giving a deterministic learning algorithm for AC 0 [p]. Approximation. ...
Acknowledgements We thank Chris Brzuska for bringing [3] to our attention, Roei Tell for helpful discussions, and the reviewers for comments that improved the presentation. ...
doi:10.4230/lipics.approx-random.2018.55
dblp:conf/approx/OliveiraS18
fatcat:gjphuxvvubakxevkekyacqxryq
Interpolated Spectral NGram Language Models
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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In this work we employ a technique for scaling up spectral learning, and use interpolated predictions that are optimized to maximize perplexity. ...
The second is that the loss function behind spectral learning, based on moment matching, differs from the probabilistic metrics used to evaluate language models. ...
Acknowledgments We are grateful to Matthias Gallé for the discussions around this work, as well as to the anonymous reviewers for their useful feedback. ...
doi:10.18653/v1/p19-1594
dblp:conf/acl/QuattoniC19
fatcat:umjd54ixobcalfga6he45bypmu
Efficiency in the Identification in the Limit Learning Paradigm
[chapter]
2016
Topics in Grammatical Inference
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Such models provide a framework to study the behavior of learning algorithms and to formally establish their soundness. ...
On the other hand, a theoretical approach is possible by using a learning paradigm, which is an attempt to formalize what learning means. ...
We extend this order to non-empty finite sets of strings: S 1 ¡ S 2 iff S 1 < S 2 or S 1 = S 2 and ∃w ∈ S 1 − S 2 such that ∀w ∈ S 2 either w ∈ S 1 or w ¡ w . ...
doi:10.1007/978-3-662-48395-4_2
fatcat:6wfzms6wy5dhjaglpxccvczqg4
Results of the Sequence PredIction ChallengE (SPiCe): a Competition on Learning the Next Symbol in a Sequence
2016
International Conference on Grammatical Inference
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The aim was to submit a ranking of the 5 most probable symbols to be the next symbol of each prefix. ...
The Sequence PredIction ChallengE (SPiCe) is an on-line competition that took place between March and July 2016. ...
Evaluation Metrics The SPiCe competition focuses on the ability of the learned models to predict the next symbol in a string. ...
dblp:conf/icgi/BalleELQV16
fatcat:oevhzjg63jeslizr5os4bfikkm
Position Models and Language Modeling
[chapter]
2008
Lecture Notes in Computer Science
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We propose here to improve the use of this model by restricting the dependency to a more reasonable value. ...
This model is not able however to capture long term dependencies, i.e. dependencies larger than n. An alternative to this model is the probabilistic automaton. ...
On the contrary to the non probabilistic case, non-deterministic automata have a greater power of expression than the deterministic one. ...
doi:10.1007/978-3-540-89689-0_12
fatcat:perwz65gsrbmflpk6hsnyi7o6i
Spectral learning of weighted automata
2013
Machine Learning
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In addition, our algorithm overcomes some of the shortcomings of previous work and is able to learn from statistics of substrings. ...
Most of these algorithms avoid the known hardness results by defining parameters beyond the number of states that can be used to quantify the complexity of learning automata under a particular distribution ...
Acknowledgements We are grateful to the anonymous reviewers for providing us with helpful comments. This work was supported by a Google Research Award, and by projects XLike (FP7-288342), BASMATI ...
doi:10.1007/s10994-013-5416-x
fatcat:gdkrhg3qpvcvzchkbwuw62j6ja
A Revision of Coding Theory for Learning from Language
2004
Electronical Notes in Theoretical Computer Science
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A differentiation 1 [1] has shown that Zipf's law is met at least by strings of independently tossed letters and spaces. [19] reports on change in the law's exponent from −1 to −3 for ranks ≈ 10 4 , which ...
Generating the full non-finitary process is a different task than short-term predicting it for the discrete classification only [33] . ...
How to classify the future and generate the full process? Short-term prediction capabilities are necessary for improving discrete linguistic classification of non-discrete acoustic percepts [27] . ...
doi:10.1016/s1571-0661(05)82574-5
fatcat:t4r6hquy6zc43ew5v7hx5eucey
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