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» Two Algorithms for Inducing Causal Models from Data
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TOSEM
1998
80views more  TOSEM 1998»
13 years 8 months ago
Discovering Models of Software Processes from Event-Based Data
Many software process methods and tools presuppose the existence of a formal model of a process. Unfortunately, developing a formal model for an on-going, complex process can be d...
Jonathan E. Cook, Alexander L. Wolf
ML
2006
ACM
121views Machine Learning» more  ML 2006»
13 years 8 months ago
Model-based transductive learning of the kernel matrix
This paper addresses the problem of transductive learning of the kernel matrix from a probabilistic perspective. We define the kernel matrix as a Wishart process prior and construc...
Zhihua Zhang, James T. Kwok, Dit-Yan Yeung
ERCIMDL
2006
Springer
204views Education» more  ERCIMDL 2006»
14 years 4 days ago
Comparing and Combining Two Approaches to Automated Subject Classification of Text
A machine-learning and a string-matching approach to automated subject classification of text were compared, as to their performance, advantages and downsides. The former approach ...
Koraljka Golub, Anders Ardö, Dunja Mladenic, ...
JCB
2007
144views more  JCB 2007»
13 years 8 months ago
Statistical Estimation of Statistical Mechanical Models: Helix-Coil Theory and Peptide Helicity Prediction
Analysis of biopolymer sequences and structures generally adopts one of two approaches: use of detailed biophysical theoretical models of the system with experimentally-determined...
Scott C. Schmidler, Joseph E. Lucas, Terrence G. O...
IFIP12
2008
13 years 10 months ago
P-Prism: A Computationally Efficient Approach to Scaling up Classification Rule Induction
Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unse...
Frederic T. Stahl, Max A. Bramer, Mo Adda