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116
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SIGCSE
1997
ACM
121views Education» more  SIGCSE 1997»
15 years 7 months ago
Application-based modules using apprentice learning for CS 2
A typical Data Structures (CS 2) course covers a wide variety of topics: elementary algorithm analysis; data structures including dynamic structures, trees, tables, graphs, etc.; ...
Owen L. Astrachan, Robert F. Smith, James T. Wilke...
127
Voted
AAAI
2007
15 years 5 months ago
Discovering Multivariate Motifs using Subsequence Density Estimation and Greedy Mixture Learning
The problem of locating motifs in real-valued, multivariate time series data involves the discovery of sets of recurring patterns embedded in the time series. Each set is composed...
David Minnen, Charles Lee Isbell Jr., Irfan A. Ess...
123
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KDD
2007
ACM
190views Data Mining» more  KDD 2007»
16 years 3 months ago
Model-shared subspace boosting for multi-label classification
Typical approaches to multi-label classification problem require learning an independent classifier for every label from all the examples and features. This can become a computati...
Rong Yan, Jelena Tesic, John R. Smith
HEURISTICS
2008
92views more  HEURISTICS 2008»
15 years 3 months ago
Learning heuristics for basic block instruction scheduling
Instruction scheduling is an important step for improving the performance of object code produced by a compiler. A fundamental problem that arises in instruction scheduling is to ...
Abid M. Malik, Tyrel Russell, Michael Chase, Peter...
ICML
2007
IEEE
16 years 4 months ago
Multifactor Gaussian process models for style-content separation
We introduce models for density estimation with multiple, hidden, continuous factors. In particular, we propose a generalization of multilinear models using nonlinear basis functi...
Jack M. Wang, David J. Fleet, Aaron Hertzmann