Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Murty, MN"'
Autor:
Athithan, Biswajit Paul G, Murty, MN
Publikováno v:
IndraStra Global.
The development of techniques for scaling up classifiers so that they can be applied to problems with large datasets of training examples is one of the objectives of data mining. Recently, AdaBoost has become popular among machine learning community
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=issn23813652::87a2aaecca7fbae5ac9dc2156ad69045
https://igi.indrastra.com/items/show/187667
https://igi.indrastra.com/items/show/187667
Autor:
Garg, Vikas K, Murty, MN
Publikováno v:
IndraStra Global.
The k-means algorithm is an extremely popular technique for clustering data. One of the major limitations of the k-means is that the time to cluster a given dataset D is linear in the number of clusters, k. In this paper, we employ height balanced tr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=issn23813652::144fa9bc2fe2967acbedd08a4776f4ab
http://eprints.iisc.ac.in/27267/1/rack.pdf
http://eprints.iisc.ac.in/27267/1/rack.pdf
Publikováno v:
IndraStra Global.
The measure-theoretic definition of Kullback-Leibler relative-entropy (or simply KL-entropy) plays a basic role in defining various classical information measures on general spaces. Entropy, mutual information and conditional forms of entropy can be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=issn23813652::0950242cb93f4a9c4817ad7d7b54e8b1
https://igi.indrastra.com/items/show/187641
https://igi.indrastra.com/items/show/187641
Autor:
Babaria, Rashmin, Saketha Nath, J, Krishnan, S, Sivaramakrishnan, KR, Bhattacharyya, Chiranjib, Murty, MN
Publikováno v:
IndraStra Global.
In this paper we propose a novel, scalable, clustering based Ordinal Regression formulation, which is an instance of a Second Order Cone Program (SOCP) with one Second Order Cone (SOC) constraint. The main contribution of the paper is a fast algorith
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=issn23813652::e72bbe91356bbc199adc4087c8ffca43
http://eprints.iisc.ac.in/41489/1/Focused_Cra.pdf
http://eprints.iisc.ac.in/41489/1/Focused_Cra.pdf
Publikováno v:
IndraStra Global.
Classification of large datasets is a challenging task in Data Mining. In the current work, we propose a novel method that compresses the data and classifies the test data directly in its compressed form. The work forms a hybrid learning approach int
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::9cbca7d5aff0866180bf6be49562beef
https://igi.indrastra.com/items/show/187893
https://igi.indrastra.com/items/show/187893
Publikováno v:
IndraStra Global.
Mining association rules from a large collection of databases is based on two main tasks. One is generation of large itemsets; and the other is finding associations between the discovered large itemsets. Existing formalism for association rules are b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=issn23813652::c5e498856805c9dbf7cd6e4c0b50a179
https://igi.indrastra.com/items/show/188281
https://igi.indrastra.com/items/show/188281
Autor:
Sridhar, V, Murty, MN
Publikováno v:
IndraStra Global.
Clustering is concerned with grouping a collection of input objects. Conventional clustering algorithms cluster unlabelled objects. We argue that there are useful applications that involve clustering of labelled objects. We propose an approach for cl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=issn23813652::c3eb93cb5a467a4294330600ca94ab6c
https://igi.indrastra.com/items/show/187366
https://igi.indrastra.com/items/show/187366
Publikováno v:
IndraStra Global.
The paper presents an extension to the standard evolutionary programming (SEP) technique, which incorporates the concept of fitness based offspring generation. Each parent in the population tries to solve the problem of surviving into future generati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=issn23813652::14f92df4ca7522c85ec1febb6c95d0f9
https://igi.indrastra.com/items/show/187491
https://igi.indrastra.com/items/show/187491
Publikováno v:
IndraStra Global.
Explores the applicability of simulated annealing, a probabilistic search method, for finding optimal partition of the data. A new formulation of the clustering problem is investigated. In order to obtain an optimal partition, a search is undertaken
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=issn23813652::875a780c7b878fbf4422a8ea21361f15
https://igi.indrastra.com/items/show/187381
https://igi.indrastra.com/items/show/187381
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