Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 14 of 14 for “"Computational learning theory"”.
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Computational learning theory : new models and algorithms
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1989.
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Topics in computational learning theory and graph algorithms
"The distribution-independent model of concept learning from examples (""PAC-learning"") due to Valiant is investigated. It has previously been shown that the existence of an Occam algorithm for a class of concepts is a sufficient condition for the PAC-learnability of that class. (An Occam …
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Matters Horn and other features in the computational learning theory landscape: The notion of membership
Current knowledge representation research has sought to provide schemes for encoding knowledge about how a given system behaves, with the goal being accuracy and utility. Ideally, the goal of encoding knowledge is not the task of encoding, but the product of the encoding task. If such encodings are …
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Geometric Concept Learning and Related Topics
… serves as an illustration of some of the useful learning paradigms that are discovered by research in computational learning theory.
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Learning Pattern Languages from a Small Number of Helpfully Chosen Examples
… introduced by Angluin in 1980. Since that time, learning of pattern languages has been a topic of great interest in the research area of computational learning theory, mainly because of its relevance for many applications. Areas in which patterns are suitable for modelling data are for example …
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A complexity theoretic approach to learning
… for attacking longstanding problems in machine learning. We use tools from computational complexity theory to make progress on problems from computational learning theory. Our methods yield the fastest and most expressive algorithms to date for learning several fundamental concept classes: * We …
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Learning diseases from data : a disease space odyssey
Recent commitments to enhance the use of data for learning in medicine provide the opportunity to apply instruments and abstractions from computational learning theory to systematize learning in medicine. The hope is to accelerate the rate at which we incorporate knowledge and improve healthcare …
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A Proposed Algorithm Toward Uniform-distribution Monotone DNF Learning
… model of Probably Approximately Correct (PAC) learning from random examples and brought up the problem of whether polynomial-size DNF functions are PAC learnable in polynomial time. It has been about twenty years that the DNF learning problem has been widely regarded as one of the most …
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Definable families of finite Vapnik Chervonenkis dimension
… notion with applications in machine learning, stability theory, and statistics. We explore what effect model theoretic structure has on the VC dimension of formulas, considered as parameterized families of sets, with respect to long disjunctions and conjunctions. If the growth in VC …
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Learning hypertrees with shortest path queries
One branch of computational learning theory focuses on algorithms for learning discrete structured objects from queries. In this context, we consider the problem of learning a labeled hypergraph from a given family of hypergraphs using shortest path (SP) queries. An SP query specifies two vertices …
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Testability of linear-invariant properties
… it have had significant impact on complexity theory, pseudorandomness, coding theory, computational learning theory, and extremal combinatorics. In the history of the area, a particularly important role has been played by linearinvariant properties, i.e., properties of Boolean functions on the …
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Computational applications of noise sensitivity
… applications of noise sensitivity: * Regarding computational hardness amplification, we prove a general direct product theorem that tightly characterizes the hardness of a composite function g 9 f in terms of an assumed hardness of f and the noise sensitivity of g. The theorem lets us prove a …
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Intelligible models for learning categorical data via generalized fourier spectrum
Machine learning techniques have found ubiquitous applications in recent years and sophisticated models such as neural networks and ensemble methods have achieved impressive predictive performances. However, these models are hard to interpret and usually used as a blackbox. In applications where an …