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» A Bayesian Approach to Tackling Hard Computational Problems
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KDD
2009
ACM
239views Data Mining» more  KDD 2009»
15 years 10 months ago
Tell me something I don't know: randomization strategies for iterative data mining
There is a wide variety of data mining methods available, and it is generally useful in exploratory data analysis to use many different methods for the same dataset. This, however...
Heikki Mannila, Kai Puolamäki, Markus Ojala, ...
KDD
2004
ACM
196views Data Mining» more  KDD 2004»
15 years 9 months ago
Adversarial classification
Essentially all data mining algorithms assume that the datagenerating process is independent of the data miner's activities. However, in many domains, including spam detectio...
Nilesh N. Dalvi, Pedro Domingos, Mausam, Sumit K. ...
KDD
2003
ACM
99views Data Mining» more  KDD 2003»
15 years 9 months ago
Fragments of order
High-dimensional collections of 0-1 data occur in many applications. The attributes in such data sets are typically considered to be unordered. However, in many cases there is a n...
Aristides Gionis, Teija Kujala, Heikki Mannila
SIGIR
2006
ACM
15 years 3 months ago
Large scale semi-supervised linear SVMs
Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In...
Vikas Sindhwani, S. Sathiya Keerthi
MIR
2010
ACM
217views Multimedia» more  MIR 2010»
15 years 2 months ago
Feature selection for content-based, time-varying musical emotion regression
In developing automated systems to recognize the emotional content of music, we are faced with a problem spanning two disparate domains: the space of human emotions and the acoust...
Erik M. Schmidt, Douglas Turnbull, Youngmoo E. Kim