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Top 10 algorithms in data mining
, 2007
"... Abstract This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, kMeans, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining a ..."
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Cited by 113 (2 self)
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Abstract This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, kMeans, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification,
Importance sampling, large deviations, and differential games
 Stoch. and Stoch. Reports
"... A heuristic that has emerged in the area of importance sampling is that the changes of measure used to prove large deviation lower bounds give good performance when used for importance sampling. Recent work, however, has suggested that the heuristic is incorrect in many situations. The perspective p ..."
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Cited by 69 (19 self)
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A heuristic that has emerged in the area of importance sampling is that the changes of measure used to prove large deviation lower bounds give good performance when used for importance sampling. Recent work, however, has suggested that the heuristic is incorrect in many situations. The perspective put forth in the present paper is that large deviation theory suggests many changes of measure, and that not all are suitable for importance sampling. In the setting of Cramérs Theorem, the traditional interpretation of the heuristic suggests a Þxed change of distribution on the underlying independent and identically distributed summands. In contrast, we consider importance sampling schemes where the exponential change of measure is adaptive, in the sense that it depends on the historical empirical mean. The existence of asymptotically optimal schemes within this class is demonstrated. The result indicates that an adaptive change of measure, rather than a static change of measure, is what the large deviations analysis truly suggests. The proofs utilize a controltheoretic approach to large deviations, which naturally leads to the construction of asymptotically optimal adaptive schemes in terms of a limit Bellman equation. Numerical examples contrasting the adaptive and standard schemes are presented, as well as an interpretation of their different performances in terms of differential games.
Dynamic importance sampling for uniformly recurrent markov chains
 Annals of Applied Probability
, 2005
"... Importance sampling is a variance reduction technique for efficient estimation of rareevent probabilities by Monte Carlo. In standard importance sampling schemes, the system is simulated using an a priori fixed change of measure suggested by a large deviation lower bound analysis. Recent work, howe ..."
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Cited by 28 (7 self)
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Importance sampling is a variance reduction technique for efficient estimation of rareevent probabilities by Monte Carlo. In standard importance sampling schemes, the system is simulated using an a priori fixed change of measure suggested by a large deviation lower bound analysis. Recent work, however, has suggested that such schemes do not work well in many situations. In this paper we consider dynamic importance sampling in the setting of uniformly recurrent Markov chains. By “dynamic ” we mean that in the course of a single simulation, the change of measure can depend on the outcome of the simulation up till that time. Based on a controltheoretic approach to large deviations, the existence of asymptotically optimal dynamic schemes is demonstrated in great generality. The implementation of the dynamic schemes is carried out with the help of a limiting Bellman equation. Numerical examples are presented to contrast the dynamic and standard schemes. 1. Introduction. Among
Adaptive Importance Sampling for Uniformly Recurrent Markov Chains
"... Importance sampling is a variance reduction technique for efficient estimation of rareevent probabilities by Monte Carlo. In standard importance sampling schemes, the system is simulated using an a priori fixed change of measure suggested by a large deviation lower bound analysis. Recent work, how ..."
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Cited by 1 (0 self)
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Importance sampling is a variance reduction technique for efficient estimation of rareevent probabilities by Monte Carlo. In standard importance sampling schemes, the system is simulated using an a priori fixed change of measure suggested by a large deviation lower bound analysis. Recent work, however, has suggested that such schemes do not work well in many situations. In this paper, we consider adaptive importance sampling in the setting of uniformly recurrent Markov chains. By “adaptive, ” we mean that the change of measure depends on the history of the samples. Based on a controltheoretic approach to large deviations, the existence of asymptotically optimal adaptive schemes is demonstrated in great generality. In this framework, the difference between a static change of measure and an adaptive change of measure amounts to the difference between an openloop control and a feedback control. The implementation of the adaptive schemes is carried out with the help of a limiting Bellman equation. Also presented are numerical examples contrasting the adaptive and standard schemes. 1
tion in High Speed Downlink Packet Access (HSDPA). Specif
"... Abstract — In this paper, we investigate throughput optimiza ..."
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DOI 10.1007/s1011500701142 SURVEY PAPER Top 10 algorithms in data mining
, 2007
"... Abstract This paper presents the top 10 data mining algorithms identified by the IEEE ..."
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Abstract This paper presents the top 10 data mining algorithms identified by the IEEE
Social Networks based eLearning Systems via Review of Recommender Systems Techniques
"... eLearning has turned to be a necessity for everyone, as it enables continuous and lifelong education. Learners are social by nature. They want to connect to othersand share the same interests. Online communities are important to help and encourage learners to continue education. Learners through s ..."
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eLearning has turned to be a necessity for everyone, as it enables continuous and lifelong education. Learners are social by nature. They want to connect to othersand share the same interests. Online communities are important to help and encourage learners to continue education. Learners through social capabilities can share different experiences.Social networks are cornerstone for eLearning. However, alternatives are many. Learners might get lost in the tremendous learning resources that are available. It is the role of recommender systems to help learners find their own way through eLearning. We present a review of different recommender system algorithms that are utilized in social networks based eLearning systems. Future research will include our proposed our eLearning system that utilizes Recommender System and Social Network.