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2,054,092
The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 13261 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
Maximum likelihood from incomplete data via the EM algorithm
- JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B
, 1977
"... A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situat ..."
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Cited by 11962 (17 self)
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A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value
Comprehensibility of Data Mining Algorithms
"... Data mining attempts to identify valid, novel, potentially useful, and ultimately understandable patterns from huge volume of data. The mined patterns must be ultimately understandable because the purpose of data mining is to aid decision-making. If the decision-makers cannot understand what does a ..."
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Data mining attempts to identify valid, novel, potentially useful, and ultimately understandable patterns from huge volume of data. The mined patterns must be ultimately understandable because the purpose of data mining is to aid decision-making. If the decision-makers cannot understand what does a
Data mining algorithms to classify students
- In Proc. of the 1st Int. Conf. on Educational Data Mining (EDM’08), p. 187191, 2008. 49 Data Mining 2009
"... Abstract. In this paper we compare different data mining methods and techniques for classifying students based on their Moodle usage data and the final marks obtained in their respective courses. We have developed a specific mining tool for making the configuration and execution of data mining techn ..."
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Cited by 40 (2 self)
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Abstract. In this paper we compare different data mining methods and techniques for classifying students based on their Moodle usage data and the final marks obtained in their respective courses. We have developed a specific mining tool for making the configuration and execution of data mining
DATA MINING ALGORITHMS FOR RANKING PROBLEMS DATA MINING ALGORITHMS FOR RANKING PROBLEMS By
"... ii Classification is the process of finding (or training) a set of models (or functions) that describe and distinguish data classes or concepts. That is for the purpose of being able to use the models to predict the unknown class labels of instances [12]. We deal with the ranking problem in this the ..."
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ii Classification is the process of finding (or training) a set of models (or functions) that describe and distinguish data classes or concepts. That is for the purpose of being able to use the models to predict the unknown class labels of instances [12]. We deal with the ranking problem
A Data Mining Algorithm for Generalized Web Prefetching
- IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
, 2003
"... Predictive Web prefetching refers to the mechanism of deducing the forthcoming page accesses of a client based on its past accesses. In this paper, we present a new context for the interpretation of Web prefetching algorithms as Markov predictors. We identify the factors that affect the performanc ..."
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Cited by 76 (16 self)
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the performance of Web prefetching algorithms. We propose a new algorithm called WM o , which is based on data mining and is proven to be a generalization of existing ones. It was designed to address their specific limitations and its characteristics include all the above factors. It compares favorably
Data Mining Algorithms for Intrusion Detection System: An Overview
"... In recent years, network based services and network based attacks have grown significantly. The network based attacks can also be considered as some kind of intrusion. Intrusion can be defined as "any set of actions that attempt to compromise the integrity, confidentiality or availability of a ..."
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Cited by 1 (0 self)
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. Several techniques such as data mining, statistics, and genetic algorithm have been used for intrusion detection. Most recently, the data mining techniques have been used to mine the normal pattern from the audit data. This paper presents the survey on data mining Algorithms applied on intrusion detection
PERFORMANCE EVALUATION AND CHARACTERIZATION OF SCALABLE DATA MINING ALGORITHMS
"... Data mining has become one of the most essential tools in diverse fields. The increases in data sizes and algorithmic complexities require the computational power of chip to increase even further. In this paper, we present detailed characteristics from the hardware and software perspectives for a se ..."
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Cited by 2 (0 self)
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Data mining has become one of the most essential tools in diverse fields. The increases in data sizes and algorithmic complexities require the computational power of chip to increase even further. In this paper, we present detailed characteristics from the hardware and software perspectives for a
Distributed Data Mining: Algorithms, Systems, and Applications
, 2002
"... This paper presents a brief overview of the DDM algorithms, systems, applications, and the emerging research directions. The structure of the paper is organized as follows. We first present the related research of DDM and illustrate data distribution scenarios. Then DDM algorithms are reviewed. Subs ..."
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Cited by 70 (5 self)
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This paper presents a brief overview of the DDM algorithms, systems, applications, and the emerging research directions. The structure of the paper is organized as follows. We first present the related research of DDM and illustrate data distribution scenarios. Then DDM algorithms are reviewed
Privacy Preserving Data Mining
- JOURNAL OF CRYPTOLOGY
, 2000
"... In this paper we address the issue of privacy preserving data mining. Specifically, we consider a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. Our work is motivated b ..."
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Cited by 523 (8 self)
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In this paper we address the issue of privacy preserving data mining. Specifically, we consider a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. Our work is motivated
Results 1 - 10
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2,054,092
