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On the Role of Search for Learning from Examples
 Journal of Experimental and Theoretical Artificial Intelligence
"... Gold [Gol67] discovered a fundamental enumeration technique, the socalled identificationbyenumeration, a simple but powerful class of algorithms for learning from examples (inductive inference). We introduce a variety of more sophisticated (and more powerful) enumeration techniques and charac ..."
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Gold [Gol67] discovered a fundamental enumeration technique, the socalled identificationbyenumeration, a simple but powerful class of algorithms for learning from examples (inductive inference). We introduce a variety of more sophisticated (and more powerful) enumeration techniques and characterize their power. We conclude with the thesis that enumeration techniques are even universal in that each solvable learning problem in inductive inference can be solved by an adequate enumeration technique. This thesis is technically motivated and discussed. Keywords: Learning from examples, learning by search, identification by enumeration, enumeration techniques. Role of Search 1 1 Introduction The role of search, for learning from examples, is examined in a theoretical setting. Gold's seminal paper [Gol67] on inductive inference introduced a simple but powerful learning technique which became known as identificationby enumeration. Identificationbyenumeration begins with an infi...
A Survey of Inductive Inference with an Emphasis on Queries
 Complexity, Logic, and Recursion Theory, number 187 in Lecture notes in Pure and Applied Mathematics Series
, 1997
"... this paper M 0 ; M 1 ; : : : is a standard list of all Turing machines, M ..."
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this paper M 0 ; M 1 ; : : : is a standard list of all Turing machines, M
Classifying Predicates and Languages
, 1997
"... The present paper studies a particular collection of classification problems, i.e., the classification of recursive predicates and languages, for arriving at a deeper understanding of what classification really is. In particular, the classification of predicates and languages is compared with the c ..."
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The present paper studies a particular collection of classification problems, i.e., the classification of recursive predicates and languages, for arriving at a deeper understanding of what classification really is. In particular, the classification of predicates and languages is compared with the classification of arbitrary recursive functions and with their learnability. The investigation undertaken is refined by introducing classification within a resource bound resulting in a new hierarchy. Furthermore, a formalization of multiclassification is presented and completely characterized in terms of standard classification. Additionally, consistent classification is introduced and compared with both resource bounded classification and standard classification. Finally, the classification of families of languages that have attracted attention in learning theory is studied, too.
Consistency Conditions for Inductive Inference of Recursive Functions
"... Abstract. A consistent learner is required to correctly and completely reflect in its actual hypothesis all data received so far. Though this demand sounds quite plausible, it may lead to the unsolvability of the learning problem. Therefore, in the present paper several variations of consistent lear ..."
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Abstract. A consistent learner is required to correctly and completely reflect in its actual hypothesis all data received so far. Though this demand sounds quite plausible, it may lead to the unsolvability of the learning problem. Therefore, in the present paper several variations of consistent learning are introduced and studied. These variations allow a socalled δ–delay relaxing the consistency demand to all but the last δ data. Additionally, we introduce the notion of coherent learning (again with δ–delay) requiring the learner to correctly reflect only the last datum (only the n − δth datum) seen. Our results are threefold. First, it is shown that all models of coherent learning with δ–delay are exactly as powerful as their corresponding consistent learning models with δ–delay. Second, we provide characterizations for consistent learning with δ–delay in terms of complexity. Finally, we establish strict hierarchies for all consistent learning models with δ–delay in dependence on δ. 1
On Uniform Learning of Classes of Recursive Functions
, 2000
"... A classical learning problem in inductive inference consists of identifying each function of a given class of recursive functions from a finite number of its output values. Uniform learning is concerned with the design of single programs solving infinitely many classical learning problems. For that ..."
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A classical learning problem in inductive inference consists of identifying each function of a given class of recursive functions from a finite number of its output values. Uniform learning is concerned with the design of single programs solving infinitely many classical learning problems. For that purpose the program reads a description of an identification problem and is supposed to construct a technique for solving the particular problem. As can be proved, uniform solvability of collections of solvable identification problems is rather influenced by the description of the problems than by the particular problems themselves. When prescribing a specific inference criterion (for example learning in the limit), a clever choice of descriptions allows uniform solvability of all solvable problems, whereas even the most simple classes of recursive functions are not learnable uniformly without restricting the set of possible descriptions. Furthermore the influence of the hypothesis spaces on uniform learnability is analysed.
Identification Criteria in Uniform Inductive Inference
"... Uniform Inductive Inference is concerned with the existence and the learning behaviour of strategies identifying infinitely many classes of recursive functions. The success of such strategies depends on the hypothesis spaces they use, as well as on the chosen identification criteria resulting from a ..."
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Uniform Inductive Inference is concerned with the existence and the learning behaviour of strategies identifying infinitely many classes of recursive functions. The success of such strategies depends on the hypothesis spaces they use, as well as on the chosen identification criteria resulting from additional demands in the basic learning model. These identification criteria correspond to different hierarchies of learning power – depending on the choice of hypothesis spaces. In most cases finite classes of recursive functions are sufficient to expose an increase in the learning power given by the uniform learning models corresponding to a pair of identification
Consistent and Coherent Learning . . .
, 2007
"... A consistent learner is required to correctly and completely reflect in its actual hypothesis all data received so far. Though this demand sounds quite plausible, it may lead to the unsolvability of the learning problem. Therefore, in the present paper several variations of consistent learning are i ..."
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A consistent learner is required to correctly and completely reflect in its actual hypothesis all data received so far. Though this demand sounds quite plausible, it may lead to the unsolvability of the learning problem. Therefore, in the present paper several variations of consistent learning are introduced and studied. These variations allow a socalled δ –delay relaxing the consistency demand to all but the last δ data. Additionally, we introduce the notion of coherent learning (again with δ – delay) requiring the learner to correctly reflect only the last datum (only the n − δ th datum) seen. Our results are manyfold. First, it is shown that all models of coherent learning with δ –delay are exactly as powerful as their corresponding consistent learning models with δ –delay. Second, we provide characterizations for consistent learning with δ –delay in terms of complexity and computable numberings. Finally, we establish strict hierarchies for all consistent learning models with δ –delay in dependence on δ.