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» Forecasting high-dimensional data
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ICDM
2006
IEEE
132views Data Mining» more  ICDM 2006»
15 years 3 months ago
High Quality, Efficient Hierarchical Document Clustering Using Closed Interesting Itemsets
High dimensionality remains a significant challenge for document clustering. Recent approaches used frequent itemsets and closed frequent itemsets to reduce dimensionality, and to...
Hassan H. Malik, John R. Kender
PREMI
2005
Springer
15 years 3 months ago
Pattern Recognition in Video
Images constitute data that lives in a very high dimensional space, typically of the order of hundred thousand dimensions. Drawing inferences from data of such high dimensions soon...
Rama Chellappa, Ashok Veeraraghavan, Gaurav Aggarw...
MM
2004
ACM
167views Multimedia» more  MM 2004»
15 years 3 months ago
Learning an image manifold for retrieval
We consider the problem of learning a mapping function from low-level feature space to high-level semantic space. Under the assumption that the data lie on a submanifold embedded ...
Xiaofei He, Wei-Ying Ma, HongJiang Zhang
KDD
2010
ACM
272views Data Mining» more  KDD 2010»
15 years 1 months ago
Beyond heuristics: learning to classify vulnerabilities and predict exploits
The security demands on modern system administration are enormous and getting worse. Chief among these demands, administrators must monitor the continual ongoing disclosure of sof...
Mehran Bozorgi, Lawrence K. Saul, Stefan Savage, G...
ESANN
2006
14 years 11 months ago
Margin based Active Learning for LVQ Networks
In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples during the model adaptation...
Frank-Michael Schleif, Barbara Hammer, Thomas Vill...