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NIPS
2007
15 years 7 months ago
Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun
ICML
2001
IEEE
16 years 6 months ago
Constrained K-means Clustering with Background Knowledge
Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data in...
Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan ...
ICML
2000
IEEE
16 years 6 months ago
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
Algorithms for feature selection fall into two broad categories: wrappers that use the learning algorithm itself to evaluate the usefulness of features and filters that evaluate f...
Mark A. Hall
KDD
2005
ACM
158views Data Mining» more  KDD 2005»
16 years 6 months ago
Adversarial learning
Many classification tasks, such as spam filtering, intrusion detection, and terrorism detection, are complicated by an adversary who wishes to avoid detection. Previous work on ad...
Daniel Lowd, Christopher Meek
KCAP
2005
ACM
15 years 11 months ago
AutoFeed: an unsupervised learning system for generating webfeeds
The AutoFeed system automatically extracts data from semistructured web sites. Previously, researchers have developed two types of supervised learning approaches for extracting we...
Bora Gazen, Steven Minton