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Your search keyword '"Pudi, Vikram"' showing total 6 results
Start Over Author "Pudi, Vikram" Topic association rule mining
6 results on '"Pudi, Vikram"'

2. ACME: An Associative Classifier Based on Maximum Entropy Principle

Author

Thonangi, Risi, Pudi, Vikram, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Jain, Sanjay, editor, Simon, Hans Ulrich, editor, and Tomita, Etsuji, editor

Published
2005
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4. A Fuzzy Associative Classification Approach to Visual Concept Detection.

Author

Mangalampalli, Ashish and Pudi, Vikram

Subjects
*FUZZY mathematics, *CLASSIFICATION algorithms, *SEMANTICS, *POLYSEMY, *ASSOCIATIVE law (Mathematics), *CLASSIFIERS (Linguistics)
Abstract

In this paper we present a novel associative classification algorithm for visual concept detection using interest points (color descriptors). Our algorithm relies on fuzzy (soft) partitions to generate a per-descriptor record model which is used for training associative classifiers. In this paper, we use the first 10000 images of the MIR Flickr dataset (with 38 visual concepts -- 14 regular annotation topics and 24 pre-annotations) for evaluating our algorithm. Because of its fuzzy nature, our approach also handles polysemy and synonymy (common problems in most crisp (non-fuzzy) image classifiers) very well. As associative classification leverages frequent patterns mined from a given dataset, its performance as adjudged from average area under curve (AUC) values over three-fold cross validation is very good, especially for regular annotation topics and for "scenic" concepts. Our approach has the added advantage that the rules used for classification have clear semantics, and can be comprehended easily, unlike other classifiers, such as SVM, which act as black-boxes. From an empirical perspective (on the MIR Flickr dataset), the performance of our algorithm is much better, than that of either bagof- words (Linear SVM and SVM RBF Kernel) or Linear SVM based on fuzzy (soft) partitions -- both benchmark approaches are based on interest points (color descriptors). [ABSTRACT FROM AUTHOR]

Published
2014
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5. Rule Synthesizing from Multiple Related Databases

Author

He, Dan, Wu, Xindong, Zhu, Xingquan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Zaki, Mohammed J., editor, Yu, Jeffrey Xu, editor, Ravindran, B., editor, and Pudi, Vikram, editor

Published
2010
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6. Mining Antagonistic Communities from Social Networks

Author

Zhang, Kuan, Lo, David, Lim, Ee-Peng, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Zaki, Mohammed J., editor, Yu, Jeffrey Xu, editor, Ravindran, B., editor, and Pudi, Vikram, editor

Published
2010
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