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Showing 1 to 7 of 7 for “"PAC Learning"”.
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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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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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Refining the sample complexity of comparative learning
The PAC (Probably Approximately Correct) framework is a well-established theoretical framework for analyzing the statistical (and sometimes computational) complexity of machine learning tasks. Comparative learning is a recently introduced variation of the PAC framework that interpolates between the …
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Quantum Algorithms and Complexity for Numerical Problems
… of problems. In addition, we study the quantum PAC learning model, offering an improved lower bound on the query complexity. The lower bound is very close to the best lower bound on query complexity known for the classical PAC learning model. We also study the algorithms and the cost of …
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On efficient learning algorithms for neural networks.
Inductive Inference Learning can be described in terms of finding a good approximation to some unknown classification rule f, based on a pre-classified set of training examples $\langle$x,f(x)$\rangle.$ One particular class of learning systems that has attracted much attention recently is the class …
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Learning in Quantum Mechanics
This thesis explores the interactions of learning and quantum mechanics. At its heart, learning consists of extracting information from data. We will consider two types of data; random and deterministic. When data is random, one usually tries to learn an approximation to a desired object, when it …
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The power and limitations of restricted quantum learning models
… are still challenges. At the same time, quantum learning theory has quickly grown and many techniques have been developed with applications in verifying and benchmarking quantum devices. However, known generic but optimal protocols may require entangled measurements across many copies of a state, …