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Hardness of Learning Problems over Burnside Groups of Exponent 3
"... In this work we investigate the hardness of a computational problem introduced in the recent work of Baumslag et al. in [3, 4]. In particular, we study the BnLHN problem, which is a generalized version of the learning with errors (LWE) problem, instantiated with a particular family of nonabelian g ..."
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In this work we investigate the hardness of a computational problem introduced in the recent work of Baumslag et al. in [3, 4]. In particular, we study the BnLHN problem, which is a generalized version of the learning with errors (LWE) problem, instantiated with a particular family of non
Hardness of Learning Problems over Burnside Groups of Exponent 3
"... In this work we investigate the hardness of a computational problem introduced in the recent work of Baumslag et al. in [5, 6]. In particular, we study the BnLHN problem, which is a generalized version of the learning with errors (LWE) problem, instantiated with a particular family of nonabelian g ..."
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abelian groups (free Burnside groups of exponent 3). In our main result, we demonstrate a random selfreducibility property for BnLHN. Along the way, we also prove a sequence of lemmas regarding homomorphisms of free Burnside groups of exponent 3 that may be of independent interest. Keywords. Random self
Machine Learning in Automated Text Categorization
 ACM COMPUTING SURVEYS
, 2002
"... The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this p ..."
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Cited by 1658 (22 self)
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to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual
Ensemble Methods in Machine Learning
 MULTIPLE CLASSIFIER SYSTEMS, LBCS1857
, 2000
"... Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include errorcorrecting output coding, Bagging, and boostin ..."
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Cited by 607 (3 self)
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Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include errorcorrecting output coding, Bagging
Digital GameBased Learning
"... [Green and Bavelier, 2003] has grabbed national attention for suggesting that playing “action ” video and computer games has the positive effect of enhancing students ’ visual selective attention. But that finding is just one small part of a more important message that all parents and educators need ..."
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Cited by 519 (0 self)
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need to hear: Video games are not the enemy, but the best opportunity we have to engage our kids in real learning.
Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations
, 2005
"... How do real graphs evolve over time? What are “normal” growth patterns in social, technological, and information networks? Many studies have discovered patterns in static graphs, identifying properties in a single snapshot of a large network, or in a very small number of snapshots; these include hea ..."
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Cited by 534 (48 self)
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heavy tails for in and outdegree distributions, communities, smallworld phenomena, and others. However, given the lack of information about network evolution over long periods, it has been hard to convert these findings into statements about trends over time. Here we study a wide range of real graphs
A learning algorithm for Boltzmann machines
 Cognitive Science
, 1985
"... The computotionol power of massively parallel networks of simple processing elements resides in the communication bandwidth provided by the hardware connections between elements. These connections con allow a significant fraction of the knowledge of the system to be applied to an instance of a probl ..."
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Cited by 586 (13 self)
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problem in o very short time. One kind of computation for which massively porollel networks appear to be well suited is large constraint satisfaction searches, but to use the connections efficiently two conditions must be met: First, a search technique that is suitable for parallel networks must be found
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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presented by Schapire in his paper "The strength of weak learnability", and represents an improvement over his results. The analysis of our algorithm provides general upper bounds on the resources required for learning in Valiant's polynomial PAC learning framework, which are the best general
Results 1  10
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604,629