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ICML
2005
IEEE
16 years 5 months ago
Active learning for Hidden Markov Models: objective functions and algorithms
Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and biosignal analysis. Active learning algorithms learn faster and/or better by cl...
Brigham Anderson, Andrew Moore
133
Voted
DMKD
2003
ACM
110views Data Mining» more  DMKD 2003»
15 years 9 months ago
Weave amino acid sequences for protein secondary structure prediction
Given a known protein sequence, predicting its secondary structure can help understand its three-dimensional (tertiary) structure, i.e., the folding. In this paper, we present an ...
Xiaochun Yang, Bin Wang
SIGKDD
2010
151views more  SIGKDD 2010»
14 years 11 months ago
Limitations of matrix completion via trace norm minimization
In recent years, compressive sensing attracts intensive attentions in the field of statistics, automatic control, data mining and machine learning. It assumes the sparsity of the ...
Xiaoxiao Shi, Philip S. Yu
KDD
2006
ACM
165views Data Mining» more  KDD 2006»
16 years 4 months ago
Training linear SVMs in linear time
Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for highdimensional sparse data commonly encountered in applications like t...
Thorsten Joachims
GECCO
2008
Springer
115views Optimization» more  GECCO 2008»
15 years 5 months ago
A genetic programming approach to business process mining
The aim of process mining is to identify and extract process patterns from data logs to reconstruct an overall process flowchart. As business processes become more and more comple...
Chris J. Turner, Ashutosh Tiwari, Jörn Mehnen