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AMAI
2004
Springer
15 years 8 months ago
Using the Central Limit Theorem for Belief Network Learning
Learning the parameters (conditional and marginal probabilities) from a data set is a common method of building a belief network. Consider the situation where we have known graph s...
Ian Davidson, Minoo Aminian
ICAI
2004
15 years 3 months ago
Inductive System Health Monitoring
- The Inductive Monitoring System (IMS) software was developed to provide a technique to automatically produce health monitoring knowledge bases for systems that are either difficu...
David L. Iverson
NAACL
2007
15 years 3 months ago
Using "Annotator Rationales" to Improve Machine Learning for Text Categorization
We propose a new framework for supervised machine learning. Our goal is to learn from smaller amounts of supervised training data, by collecting a richer kind of training data: an...
Omar Zaidan, Jason Eisner, Christine D. Piatko
134
Voted
GRC
2008
IEEE
15 years 3 months ago
Neighborhood Smoothing Embedding for Noisy Manifold Learning
Manifold learning can discover the structure of high dimensional data and provides understanding of multidimensional patterns by preserving the local geometric characteristics. Ho...
Guisheng Chen, Junsong Yin, Deyi Li
BSN
2006
IEEE
131views Sensor Networks» more  BSN 2006»
15 years 8 months ago
Elaborating Sensor Data using Temporal and Spatial Commonsense Reasoning
Ubiquitous computing has established a vision of computation where computers are so deeply integrated into our lives that they become both invisible and everywhere. In order to ha...
Bo Morgan, Push Singh