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Computational Limits on Team Identification of Languages
, 1993
"... A team of learning machines is essentially a multiset of learning machines. ..."
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A team of learning machines is essentially a multiset of learning machines.
On Aggregating Teams of Learning Machines
 Theoretical Computer Science A
, 1994
"... The present paper studies the problem of when a team of learning machines can be aggregated into a single learning machine without any loss in learning power. The main results concern aggregation ratios for vacillatory identification of languages from texts. For a positiveinteger n,amachine is said ..."
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Cited by 9 (4 self)
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The present paper studies the problem of when a team of learning machines can be aggregated into a single learning machine without any loss in learning power. The main results concern aggregation ratios for vacillatory identification of languages from texts. For a positiveinteger n,amachine is said to TxtFex n identify a language L just in case the machine converges to up to n grammars for L on any text for L.For such identification criteria, the aggregation ratio is derived for the n = 2 case. It is shown that the collection of languages that can be TxtFex 2 identified by teams with success ratio greater than 5=6 are the same as those collections of languages that can be TxtFex 2  identified by a single machine. It is also established that 5=6 is indeed the cutoff point by showing that there are collections of languages that can be TxtFex 2 identified bya team employing 6 machines, at least 5 of which are required to be successful, but cannot be TxtFex 2 identified byany single machine. Additionally, aggregation ratios are also derived for finite identification of languages from positive data and for numerous criteria involving language learning from both positive and negative data.
Training Sequences
"... this paper initiates a study in which it is demonstrated that certain concepts (represented by functions) can be learned, but only in the event that certain relevant subconcepts (also represented by functions) have been previously learned. In other words, the Soar project presents empirical evidence ..."
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Cited by 9 (1 self)
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this paper initiates a study in which it is demonstrated that certain concepts (represented by functions) can be learned, but only in the event that certain relevant subconcepts (also represented by functions) have been previously learned. In other words, the Soar project presents empirical evidence that learning how to learn is viable for computers and this paper proves that doing so is the only way possible for computers to make certain inferences.
On identification by teams and probabilistic machines
 Lecture Notes in Artificial Intelligence
, 1995
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Recursion Theoretic Models of Learning: Some Results and Intuitions
 Annals of Mathematics and Artificial Intelligence
, 1995
"... View of Learning To implement a program that somehow "learns" it is neccessary to fix a set of concepts to be learned and develop a representation for the concepts and examples of the concepts. In order to investigate general properties of machine learning it is neccesary to work in as re ..."
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Cited by 4 (2 self)
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View of Learning To implement a program that somehow "learns" it is neccessary to fix a set of concepts to be learned and develop a representation for the concepts and examples of the concepts. In order to investigate general properties of machine learning it is neccesary to work in as representation independent fashion as possible. In this work, we consider machines that learn programs for recursive functions. Several authors have argued that such studies are general enough to include a wide array of learning situations [2,3,22,23,24]. For example, a behavior to be learned can be modeled as a set of stimulus and response pairs. Assuming that any behavior associates only one response to each possible stimulus, behaviors can be viewed as functions from stimuli to responses. Some behaviors, such as anger, are not easily modeled as functions. Our primary interest, however, concerns the learning of fundamental behaviors such as reading (mapping symbols to sounds), recognition (mapping pa...
Team Learning of Computable Languages
"... A team of learning machines is a multiset of learning machines. A team is said to successfully learn a concept just in case each member of some nonempty subset, of predetermined size, of the team learns the concept. Team learning of languages may be viewed as a suitable theoretical model for studyin ..."
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A team of learning machines is a multiset of learning machines. A team is said to successfully learn a concept just in case each member of some nonempty subset, of predetermined size, of the team learns the concept. Team learning of languages may be viewed as a suitable theoretical model for studying computational limits on the use of multiple heuristics in learning from examples. Team learning of recursively enumerable languages has been studied extensively. However, it may be argued that from a practical point of view all languages of interest are computable. This paper gives theoretical results about team learnability of computable (recursive) languages. These results are mainly about two issues: redundancy and aggregation. The issue of redundancy deals with the impact of increasing the size of a team and increasing the number of machines required to be successful. The issue of aggregation deals with conditions under which a team may be replaced by a single machine without any loss in learning ability. The learning scenarios considered are: (a) Identification in the limit of grammars for computable languages. (b) Identification in the limit of decision procedures for computable languages. (c) Identification in the limit of grammars for indexed families of computable languages. (d) Identification in the limit of grammars for indexed families with a recursively enumerable class of grammars for the family as the hypothesis space. Scenarios that can be modeled by team learning are also presented. 1
Machine Learning of Inflection
"... The task of learning a natural language is an instance of inductive inference (making generalizations based on the past observations to predict the future ones) studied within several traditions in the sciences under such names as “machine learning, ” “grammar induction,” and “computational learnin ..."
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The task of learning a natural language is an instance of inductive inference (making generalizations based on the past observations to predict the future ones) studied within several traditions in the sciences under such names as “machine learning, ” “grammar induction,” and “computational learning theory. ” In this article, I aim to introduce some of the con
On Learning To Coordinate: Random Bits Help, Insightful Normal Forms, and Competency Isomorphisms
, 2007
"... A mere bounded number of random bits judiciously employed by a probabilistically correct algorithmic coordinator is shown to increase the power of learning to coordinate compared to deterministic algorithmic coordinators. Furthermore, these probabilistic algorithmic coordinators are provably not cha ..."
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A mere bounded number of random bits judiciously employed by a probabilistically correct algorithmic coordinator is shown to increase the power of learning to coordinate compared to deterministic algorithmic coordinators. Furthermore, these probabilistic algorithmic coordinators are provably not characterized in power by teams of deterministic ones. An insightful, enumeration technique based, normal form characterization of the classes that are learnable by total computable coordinators is given. These normal forms are for insight only since it is shown that the complexity of the normal form of a total computable coordinator can be infeasible compared to the original coordinator. Montagna and Osherson showed that the competence class of a total coordinator cannot be strictly improved by another total coordinator. It is shown in the present paper that the competencies of any two total coordinators are the same modulo isomorphism. Furthermore, a completely effective, index set version of this competency isomorphism result is given, where all the coordinators are total computable. We also investigate the competence classes of total coordinators from the points of view of topology and descriptive set theory.