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• Hypothesis Class: H
"... {0, 1}valued random variables X1,..., Xn are drawn independently each from Bernoulli distribution with parameter p = 0.1. Define Pn: = P ( 1n ∑n i=1Xi ≤ 0.2). (a) For n = 1 to 30 calculate and plot the below in the same plot (see [1, section 6.1] for definition of Hoeffding and Bernstein inequaliti ..."
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{0, 1}valued random variables X1,..., Xn are drawn independently each from Bernoulli distribution with parameter p = 0.1. Define Pn: = P ( 1n ∑n i=1Xi ≤ 0.2). (a) For n = 1 to 30 calculate and plot the below in the same plot (see [1, section 6.1] for definition of Hoeffding and Bernstein inequalities): i. Exact value of Pn (binomial distribution). ii. Normal approximation for Pn. iii. Hoeffding inequality bound on Pn. iv. Bernstein inequality bound on Pn. (b) For n = 30 to 300 calculate and plot the below in the same plot: i. Normal approximation for Pn. ii. Hoeffding inequality bound on Pn. iii. Bernstein inequality bound on Pn. 2. VC Bound:
Learning Agents with Evolving Hypothesis Classes
"... Abstract. It has recently been shown that a Bayesian agent with a universal hypothesis class resolves most induction problems discussed in the philosophy of science. These ideal agents are, however, neither practical nor a good model for how real science works. We here introduce a framework for lear ..."
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Abstract. It has recently been shown that a Bayesian agent with a universal hypothesis class resolves most induction problems discussed in the philosophy of science. These ideal agents are, however, neither practical nor a good model for how real science works. We here introduce a framework
N.: Multiinstance learning with any hypothesis class
, 2011
"... In the supervised learning setting termed MultipleInstance Learning (MIL), the examples are bags of instances, and the bag label is a function of the labels of its instances. Typically, this function is the Boolean OR. The learner observes a sample of bags and the bag labels, but not the instance l ..."
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Cited by 5 (0 self)
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provide a unified theoretical analysis for MIL, which holds for any underlying hypothesis class, regardless of a specific application or problem domain. We show that the sample complexity of MIL is only polylogarithmically dependent on the size of the bag, for any underlying hypothesis class. In addition
The strength of weak learnability
 MACHINE LEARNING
, 1990
"... This paper addresses the problem of improving the accuracy of an hypothesis output by a learning algorithm in the distributionfree (PAC) learning model. A concept class is learnable (or strongly learnable) if, given access to a Source of examples of the unknown concept, the learner with high prob ..."
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Cited by 871 (26 self)
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This paper addresses the problem of improving the accuracy of an hypothesis output by a learning algorithm in the distributionfree (PAC) learning model. A concept class is learnable (or strongly learnable) if, given access to a Source of examples of the unknown concept, the learner with high
Learning quickly when irrelevant attributes abound: A new linearthreshold algorithm
 Machine Learning
, 1988
"... learning Boolean functions, linearthreshold algorithms Abstract. Valiant (1984) and others have studied the problem of learning various classes of Boolean functions from examples. Here we discuss incremental learning of these functions. We consider a setting in which the learner responds to each ex ..."
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Cited by 773 (5 self)
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example according to a current hypothesis. Then the learner updates the hypothesis, if necessary, based on the correct classification of the example. One natural measure of the quality of learning in this setting is the number of mistakes the learner makes. For suitable classes of functions, learning
Generalizing apprenticeship learning across hypothesis classes
 In ICML
, 2010
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 19 (10 self)
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Motivation through the Design of Work: Test of a Theory. Organizational Behavior and Human Performance,
, 1976
"... A model is proposed that specifies the conditions under which individuals will become internally motivated to perform effectively on their jobs. The model focuses on the interaction among three classes of variables: (a) the psychological states of employees that must be present for internally motiv ..."
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Cited by 622 (2 self)
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A model is proposed that specifies the conditions under which individuals will become internally motivated to perform effectively on their jobs. The model focuses on the interaction among three classes of variables: (a) the psychological states of employees that must be present for internally
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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nothing directly to do with coding or decoding will show that in some sense belief propagation "converges with high probability to a nearoptimum value" of the desired belief on a class of loopy DAGs Progress in the analysis of loopy belief propagation has been made for the case of networks
Machine Learning with Data Dependent Hypothesis Classes
 Journal of Machine Learning Research
, 2002
"... We extend the VC theory of statistical learning to data dependent spaces of classifiers. ..."
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Cited by 11 (0 self)
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We extend the VC theory of statistical learning to data dependent spaces of classifiers.
Results 1  10
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