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197
Adaptive Disk Spindown via Optimal RenttoBuy in Probabilistic Environments
, 1999
"... In the single renttobuy decision problem, without a priori knowledge of the amount of time a resource will be used we need to decide when to buy the resource, given that we can rent the resource for $1 per unit time or buy it once and for all for $c. In this paper we study algorithms that make a ..."
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Cited by 83 (4 self)
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In the single renttobuy decision problem, without a priori knowledge of the amount of time a resource will be used we need to decide when to buy the resource, given that we can rent the resource for $1 per unit time or buy it once and for all for $c. In this paper we study algorithms that make a sequence of single renttobuy decisions, using the assumption that the resource use times are independently drawn from an unknown probability distribution. Our study of this renttobuy problem is motivated by important systems applications, specifically, problems arising from deciding when to spindown disks to conserve energy in mobile computers [4], [13], [15], thread blocking decisions during lock acquisition in multiprocessor applications [7], and virtual circuit holding times in IPoverATM networks [11], [19]. We develop a provably optimal and computationally efficient algorithm for the renttobuy problem. Our algorithm uses O ( √ t) time and space, and its expected cost for the tth resource use converges to optimal as O ( √ log t/t), for any bounded probability distribution on the resource use times. Alternatively, using O(1) time and space, the algorithm almost converges to optimal. We describe the experimental results for the application of our algorithm to one of the motivating systems problems: the question of when to spindown a disk to save power in a mobile computer. Simulations using disk access traces obtained from an HP workstation environment suggest that our algorithm yields significantly improved power/response time performance over the nonadaptive 2competitive algorithm which is optimal in the worstcase competitive analysis model.
Yago: A Large Ontology from Wikipedia and WordNet
, 2007
"... This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic IsA hierarchy a ..."
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Cited by 79 (11 self)
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This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic IsA hierarchy as well as semantic relations between entities. The facts for YAGO have been extracted from the category system and the infoboxes of Wikipedia and have been combined with taxonomic relations from WordNet. Type checking techniques help us keep YAGO’s precision at 95% – as proven by an extensive evaluation study. YAGO is based on a clean logical model with a decidable consistency. Furthermore, it allows representing nary relations in a natural way while maintaining compatibility with RDFS. A powerful query model facilitates access to YAGO’s data.
Learning Action Strategies for Planning Domains
 ARTIFICIAL INTELLIGENCE
, 1997
"... This paper reports on experiments where techniques of supervised machine learning are applied to the problem of planning. The input to the learning algorithm is composed of a description of a planning domain, planning problems in this domain, and solutions for them. The output is an efficient algori ..."
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Cited by 71 (3 self)
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This paper reports on experiments where techniques of supervised machine learning are applied to the problem of planning. The input to the learning algorithm is composed of a description of a planning domain, planning problems in this domain, and solutions for them. The output is an efficient algorithm  a strategy  for solving problems in that domain. We test the strategy on an independent set of planning problems from the same domain, so that success is measured by its ability to solve complete problems. A system, L2Act, has been developed in order to perform these experiments. We have experimented with the blocks world domain, and the logistics domain, using strategies in the form of a generalization of decision lists, where the rules on the list are existentially quantified first order expressions. The learning algorithm is a variant of Rivest`s [39] algorithm, improved with several techniques that reduce its time complexity. As the experiments demonstrate, generalization is a...
Efficient Agnostic Learning of Neural Networks with Bounded Fanin
, 1996
"... We show that the class of two layer neural networks with bounded fanin is efficiently learnable in a realistic extension to the Probably Approximately Correct (PAC) learning model. In this model, a joint probability distribution is assumed to exist on the observations and the learner is required to ..."
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Cited by 68 (18 self)
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We show that the class of two layer neural networks with bounded fanin is efficiently learnable in a realistic extension to the Probably Approximately Correct (PAC) learning model. In this model, a joint probability distribution is assumed to exist on the observations and the learner is required to approximate the neural network which minimizes the expected quadratic error. As special cases, the model allows learning realvalued functions with bounded noise, learning probabilistic concepts and learning the best approximation to a target function that cannot be well approximated by the neural network. The networks we consider have realvalued inputs and outputs, an unlimited number of threshold hidden units with bounded fanin, and a bound on the sum of the absolute values of the output weights. The number of computation This work was supported by the Australian Research Council and the Australian Telecommunications and Electronics Research Board. The material in this paper was pres...
Fatshattering and the learnability of realvalued functions
 Journal of Computer and System Sciences
, 1996
"... We consider the problem of learning realvalued functions from random examples when the function values are corrupted with noise. With mild conditions on independent observation noise, we provide characterizations of the learnability of a realvalued function class in terms of a generalization of th ..."
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Cited by 61 (10 self)
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We consider the problem of learning realvalued functions from random examples when the function values are corrupted with noise. With mild conditions on independent observation noise, we provide characterizations of the learnability of a realvalued function class in terms of a generalization of the VapnikChervonenkis dimension, the fatshattering function, introduced by Kearns and Schapire. We show that, given some restrictions on the noise, a function class is learnable in our model if and only if its fatshattering function is finite. With different (also quite mild) restrictions, satisfied for example by gaussian noise, we show that a function class is learnable from polynomially many examples if and only if its fatshattering function grows polynomially. We prove analogous results in an agnostic setting, where there is no assumption of an underlying function class. 1
On Randomized OneRound Communication Complexity
 Computational Complexity
, 1995
"... We present several results regarding randomized oneround communication complexity. Our results include a connection to the VCdimension, a study of the problem of computing the inner product of two real valued vectors, and a relation between \simultaneous" protocols and oneround protocols. Ke ..."
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Cited by 61 (0 self)
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We present several results regarding randomized oneround communication complexity. Our results include a connection to the VCdimension, a study of the problem of computing the inner product of two real valued vectors, and a relation between \simultaneous" protocols and oneround protocols. Key words. Communication Complexity; Oneround and simultaneous protocols; VCdimension; Subject classications. 68Q25. 1.
On Learning from MultiInstance Examples: Empirical Evaluation of a theoretical Approach
, 1997
"... ..."
A unified framework for Regularization Networks and Support Vector Machines
, 1999
"... This report describers research done at the Center for Biological & Computational Learning and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. This research was sponsored by theN ational Science Foundation under contractN o. IIS9800032, the O#ce ofN aval Res ..."
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Cited by 51 (13 self)
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This report describers research done at the Center for Biological & Computational Learning and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. This research was sponsored by theN ational Science Foundation under contractN o. IIS9800032, the O#ce ofN aval Research under contractN o.N 0001493 10385 and contractN o.N 000149510600. Partial support was also provided by DaimlerBenz AG, Eastman Kodak, Siemens Corporate Research, Inc., ATR and AT&T. Contents Introductic 3 2 OverviF of stati.48EF learni4 theory 5 2.1 Unifo6 Co vergence and the VapnikChervo nenkis bo und ............. 7 2.2 The metho d o Structural Risk Minimizatio ..................... 10 2.3 #unifo8 co vergence and the V # ..................... 10 2.4 Overviewo fo urappro6 h ............................... 13 3 Reproduci9 Kernel HiT ert Spaces: a briL overviE 14 4RegulariEqq.L Networks 16 4.1 Radial Basis Functio8 ................................. 19 4.2 Regularizatioz generalized splines and kernel smo oxy rs .............. 20 4.3 Dual representatio o f Regularizatio Netwo rks ................... 21 4.4 Fro regressioto 5 Support vector machiT9 22 5.1 SVMin RKHS ..................................... 22 5.2 Fro regressioto 6SRMforRNsandSVMs 26 6.1 SRMfo SVMClassificatio .............................. 28 6.1.1 Distributio dependent bo undsfo SVMC .................. 29 7 A BayesiL Interpretatiq ofRegulariTFqEL and SRM? 30 7.1 Maximum A Po terio6 Interpretatio o f ............... 30 7.2 Bayesian interpretatio o f the stabilizer in the RN andSVMfunctio6I6 ...... 32 7.3 Bayesian interpretatio o f the data term in the Regularizatio andSVMfunctioy8 33 7.4 Why a MAP interpretatio may be misleading .................... 33 Connectine between SVMs and Sparse Ap...
Learning to Take Actions
, 1998
"... We formalize a model for supervised learning of action strategies in dynamic stochastic domains and show that PAClearning results on Occam algorithms hold in this model as well. We then identify a class of rulebased action strategies for which polynomial time learning is possible. The representati ..."
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Cited by 50 (8 self)
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We formalize a model for supervised learning of action strategies in dynamic stochastic domains and show that PAClearning results on Occam algorithms hold in this model as well. We then identify a class of rulebased action strategies for which polynomial time learning is possible. The representation of strategies is a generalization of decision lists; strategies include rules with existentially quantified conditions, simple recursive predicates, and small internal state, but are syntactically restricted. We also study the learnability of hierarchically composed strategies where a subroutine already acquired can be used as a basic action in a higher level strategy. We prove some positive results in this setting, but also show that in some cases the hierarchical learning problem is computationally hard. 1 Introduction We formalize a model for supervised learning of action strategies in dynamic stochastic domains, and study the learnability of strategies represented by rulebased syste...
Knows What It Knows: A Framework For SelfAware Learning
"... We introduce a learning framework that combines elements of the wellknown PAC and mistakebound models. The KWIK (knows what it knows) framework was designed particularly for its utility in learning settings where active exploration can impact the training examples the learner is exposed to, as is ..."
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Cited by 45 (16 self)
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We introduce a learning framework that combines elements of the wellknown PAC and mistakebound models. The KWIK (knows what it knows) framework was designed particularly for its utility in learning settings where active exploration can impact the training examples the learner is exposed to, as is true in reinforcementlearning and activelearning problems. We catalog several KWIKlearnable classes and open problems. 1.