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52
Oracle quantum computing
 Brassard & U.Vazirani, Strengths and weaknesses of quantum computing
, 1994
"... \Because nature isn't classical, dammit..." ..."
Biometric identification
 Communications of the ACM
, 2000
"... Identification of grammars (r. e. indices) for recursively enumerable languages from positive data by algorithmic devices is a well studied problem in learning theory. The present paper considers identification of r. e. languages by machines that have access to membership oracles for noncomputable s ..."
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Cited by 60 (4 self)
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Identification of grammars (r. e. indices) for recursively enumerable languages from positive data by algorithmic devices is a well studied problem in learning theory. The present paper considers identification of r. e. languages by machines that have access to membership oracles for noncomputable sets. It is shown that for any set A there exists another set B such that the collections of r. e. languages that can be identified by machines with access to a membership oracle for B is strictly larger than the collections of r. e. languages that can be identified by machines with access to a membership oracle for A. In other words, there is no maximal inference degree for language identification.
The Power of Vacillation in Language Learning
, 1992
"... Some extensions are considered of Gold's influential model of language learning by machine from positive data. Studied are criteria of successful learning featuring convergence in the limit to vacillation between several alternative correct grammars. The main theorem of this paper is that there are ..."
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Cited by 44 (11 self)
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Some extensions are considered of Gold's influential model of language learning by machine from positive data. Studied are criteria of successful learning featuring convergence in the limit to vacillation between several alternative correct grammars. The main theorem of this paper is that there are classes of languages that can be learned if convergence in the limit to up to (n+1) exactly correct grammars is allowed but which cannot be learned if convergence in the limit is to no more than n grammars, where the no more than n grammars can each make finitely many mistakes. This contrasts sharply with results of Barzdin and Podnieks and, later, Case and Smith, for learnability from both positive and negative data. A subset principle from a 1980 paper of Angluin is extended to the vacillatory and other criteria of this paper. This principle, provides a necessary condition for circumventing overgeneralization in learning from positive data. It is applied to prove another theorem to the eff...
Incremental concept learning for bounded data mining
 Information and Computation
, 1999
"... Important re nements of concept learning in the limit from positive data considerably restricting the accessibility of input data are studied. Let c be any concept; every in nite sequence of elements exhausting c is called positive presentation of c. In all learning models considered the learning ma ..."
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Cited by 39 (29 self)
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Important re nements of concept learning in the limit from positive data considerably restricting the accessibility of input data are studied. Let c be any concept; every in nite sequence of elements exhausting c is called positive presentation of c. In all learning models considered the learning machine computes a sequence of hypotheses about the target concept from a positive presentation of it. With iterative learning, the learning machine, in making a conjecture, has access to its previous conjecture and the latest data item coming in. In kbounded examplememory inference (k is a priori xed) the learner is allowed to access, in making a conjecture, its previous hypothesis, its memory of up to k data items it has already seen, and the next element coming in. In the case of kfeedback identi cation, the learning machine, in making a conjecture, has access to its previous conjecture, the latest data item coming in, and, on the basis of this information, it can compute k items and query the database of previous data to nd out, for each of the k items, whether or not it is in the database (k is again a priori xed). In all cases, the sequence of conjectures has to converge to a hypothesis
Incremental Learning from Positive Data
 Journal of Computer and System Sciences
, 1996
"... The present paper deals with a systematic study of incremental learning algorithms. ..."
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Cited by 32 (19 self)
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The present paper deals with a systematic study of incremental learning algorithms.
Syntactic Measures of Complexity
, 1999
"... page 14 Declaration  page 15 Notes of copyright and the ownership of intellectual property rights  page 15 The Author  page 16 Acknowledgements  page 16 1  Introduction  page 17 1.1  Background  page 17 1.2  The Style of Approach  page 18 1.3  Motivation  page 19 1.4  Style of ..."
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Cited by 23 (2 self)
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page 14 Declaration  page 15 Notes of copyright and the ownership of intellectual property rights  page 15 The Author  page 16 Acknowledgements  page 16 1  Introduction  page 17 1.1  Background  page 17 1.2  The Style of Approach  page 18 1.3  Motivation  page 19 1.4  Style of Presentation  page 20 1.5  Outline of the Thesis  page 21 2  Models and Modelling  page 23 2.1  Some Types of Models  page 25 2.2  Combinations of Models  page 28 2.3  Parts of the Modelling Apparatus  page 33 2.4  Models in Machine Learning  page 38 2.5  The Philosophical Background to the Rest of this Thesis  page 41 Syntactic Measures of Complexity  page 3  3  Problems and Properties  page 44 3.1  Examples of Common Usage  page 44 3.1.1  A case of nails  page 44 3.1.2  Writing a thesis  page 44 3.1.3  Mathematics  page 44 3.1.4  A gas  page 44 3.1.5  An ant hill  page 45 3.1.6  A car engine  page 45 3.1.7  A cell as part of an organism ...
Ignoring Data May be the Only Way to Learn Efficiently
, 1994
"... In designing learning algorithms it seems quite reasonable to construct them in a way such that all data the algorithm already has obtained are correctly and completely reflected in the hypothesis the algorithm outputs on these data. However, this approach may totally fail, i.e., it may lead to t ..."
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Cited by 19 (13 self)
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In designing learning algorithms it seems quite reasonable to construct them in a way such that all data the algorithm already has obtained are correctly and completely reflected in the hypothesis the algorithm outputs on these data. However, this approach may totally fail, i.e., it may lead to the unsolvability of the learning problem, or it may exclude any efficient solution of it. In particular, we present a natural learning problem and prove that it can be solved in polynomial time if and only if the algorithm is allowed to ignore data.
Infinitary Self Reference in Learning Theory
, 1994
"... Kleene's Second Recursion Theorem provides a means for transforming any program p into a program e(p) which first creates a quiescent self copy and then runs p on that self copy together with any externally given input. e(p), in effect, has complete (low level) self knowledge, and p represents how ..."
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Cited by 18 (6 self)
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Kleene's Second Recursion Theorem provides a means for transforming any program p into a program e(p) which first creates a quiescent self copy and then runs p on that self copy together with any externally given input. e(p), in effect, has complete (low level) self knowledge, and p represents how e(p) uses its self knowledge (and its knowledge of the external world). Infinite regress is not required since e(p) creates its self copy outside of itself. One mechanism to achieve this creation is a self replication trick isomorphic to that employed by singlecelled organisms. Another is for e(p) to look in a mirror to see which program it is. In 1974 the author published an infinitary generalization of Kleene's theorem which he called the Operator Recursion Theorem. It provides a means for obtaining an (algorithmically) growing collection of programs which, in effect, share a common (also growing) mirror from which they can obtain complete low level models of themselves and the other prog...
Computational Limits on Team Identification of Languages
, 1993
"... A team of learning machines is essentially a multiset of learning machines. ..."
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Cited by 17 (7 self)
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A team of learning machines is essentially a multiset of learning machines.
An overview of computational complexity
 Communications of the ACM
, 1983
"... foremost recognition of technical contributions to the computing community. The citation of Cook's achievements noted that "Dr. Cook has advanced our understanding of the complexity of computation in a significant and profound way. His seminal paper, The Complexity of Theorem Proving Procedures ..."
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Cited by 17 (0 self)
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foremost recognition of technical contributions to the computing community. The citation of Cook's achievements noted that "Dr. Cook has advanced our understanding of the complexity of computation in a significant and profound way. His seminal paper, The Complexity of Theorem Proving Procedures, presented at the 1971 ACM SIGACT Symposium on the Theory of Computing, laid the foundations for the theory of NPcompleteness. The ensuing exploration of the boundaries and nature of the NPcomplete class of problems has been one of the most active and important research activities in computer science for the last decade. Cook is well known for his influential results in fundamental areas of computer science. He has made significant contributions to complexity theory, to timespace tradeoffs in computation, and to logics for programming languages. His work is characterized by elegance and insights and has illuminated the very nature of computation." During 19701979, Cook did extensive work under grants from the