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Efficient Learning with Virtual Threshold Gates
 Information and Computation
, 1997
"... We reduce learning simple geometric concept classes to learning disjunctions over exponentially many variables. We then apply an online algorithm called Winnow whose number of prediction mistakes grows only logarithmically with the number of variables. The hypotheses of Winnow are linear thresho ..."
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Cited by 57 (5 self)
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threshold functions with one weight per variable. We find ways to keep the exponentially many weights of Winnow implicitly so that the time for the algorithm to compute a prediction and update its "virtual" weights is polynomial. Our method can be used to learn ddimensional axisparallel boxes
Efficient Learning with Virtual Threshold Gates
 Information and Computation
"... this paper is to reduce learning particular concept classes to the case of learning disjunctions or more generally linear threshold functions over exponentially many variables. Then the algorithm Winnow [Lit88] is applied which learns for example kliteral monotone disjunctions over v variables with ..."
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this paper is to reduce learning particular concept classes to the case of learning disjunctions or more generally linear threshold functions over exponentially many variables. Then the algorithm Winnow [Lit88] is applied which learns for example kliteral monotone disjunctions over v variables
Article No. IC972686 Efficient Learning with Virtual Threshold Gates*
"... Email: manfred cis.ucsc.edu We reduce learning simple geometric concept classes to learning disjunctions over exponentially many variables. We then apply an online algorithm called Winnow whose number of prediction mistakes grows only logarithmically with the number of variables. The hypotheses of ..."
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of Winnow are linear threshold functions with one weight per variable. We find ways to keep the exponentially many weights of Winnow implicitly so that the time for the algorithm to compute a prediction and update its ``virtual' ' weights is polynomial. Our method can be used to learn d
Live Migration of Virtual Machines
 In Proceedings of the 2nd ACM/USENIX Symposium on Networked Systems Design and Implementation (NSDI
, 2005
"... Migrating operating system instances across distinct physical hosts is a useful tool for administrators of data centers and clusters: It allows a clean separation between hardware and software, and facilitates fault management, load balancing, and lowlevel system maintenance. By carrying out the ma ..."
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Cited by 613 (14 self)
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Migrating operating system instances across distinct physical hosts is a useful tool for administrators of data centers and clusters: It allows a clean separation between hardware and software, and facilitates fault management, load balancing, and lowlevel system maintenance. By carrying out the majority of migration while OSes continue to run, we achieve impressive performance with minimal service downtimes; we demonstrate the migration of entire OS instances on a commodity cluster, recording service downtimes as low as 60ms. We show that that our performance is sufficient to make live migration a practical tool even for servers running interactive loads. In this paper we consider the design options for migrating OSes running services with liveness constraints, focusing on data center and cluster environments. We introduce and analyze the concept of writable working set, and present the design, implementation and evaluation of highperformance OS migration built on top of the Xen VMM. 1
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 780 (5 self)
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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
Maté: A Tiny Virtual Machine for Sensor Networks
, 2002
"... Composed of tens of thousands of tiny devices with very limited resources ("motes"), sensor networks are subject to novel systems problems and constraints. The large number of motes in a sensor network means that there will often be some failing nodes; networks must be easy to repopulate. ..."
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Cited by 502 (21 self)
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late. Often there is no feasible method to recharge motes, so energy is a precious resource. Once deployed, a network must be reprogrammable although physically unreachable, and this reprogramming can be a significant energy cost. We present Maté, a tiny communicationcentric virtual machine designed
Boosting a Weak Learning Algorithm By Majority
, 1995
"... We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training the given learning algorithm on a different set of examples. Our algorithm is based on ideas pr ..."
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Cited by 516 (15 self)
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We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training the given learning algorithm on a different set of examples. Our algorithm is based on ideas
What Can Economists Learn from Happiness Research?
 FORTHCOMING IN JOURNAL OF ECONOMIC LITERATURE
, 2002
"... Happiness is generally considered to be an ultimate goal in life; virtually everybody wants to be happy. The United States Declaration of Independence of 1776 takes it as a selfevident truth that the “pursuit of happiness” is an “unalienable right”, comparable to life and liberty. It follows that e ..."
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Cited by 517 (24 self)
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Happiness is generally considered to be an ultimate goal in life; virtually everybody wants to be happy. The United States Declaration of Independence of 1776 takes it as a selfevident truth that the “pursuit of happiness” is an “unalienable right”, comparable to life and liberty. It follows
Thresholding of statistical maps in functional neuroimaging using the false discovery rate
 Neuroimage
, 2002
"... Finding objective and effective thresholds for voxelwise statistics derived from neuroimaging data has been a longstanding problem. With at least one test performed for every voxel in an image, some correction of the thresholds is needed to control the error rates, but standard procedures for multi ..."
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Cited by 494 (8 self)
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Finding objective and effective thresholds for voxelwise statistics derived from neuroimaging data has been a longstanding problem. With at least one test performed for every voxel in an image, some correction of the thresholds is needed to control the error rates, but standard procedures
Results 1  10
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761,421