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KDD
2010
ACM
224views Data Mining» more  KDD 2010»
15 years 8 months ago
Multi-label learning by exploiting label dependency
In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for the unseen example. Due to the tremendous (ex...
Min-Ling Zhang, Kun Zhang
ACCV
2010
Springer
15 years 6 months ago
Identifying Surprising Events in Videos Using Bayesian Topic Models
Automatic processing of video data is essential in order to allow efficient access to large amounts of video content, a crucial point in such applications as video mining and surve...
Avishai Hendel, Daphna Weinshall, Shmuel Peleg
DMIN
2007
186views Data Mining» more  DMIN 2007»
15 years 5 months ago
Cost-Sensitive Learning vs. Sampling: Which is Best for Handling Unbalanced Classes with Unequal Error Costs?
- The classifier built from a data set with a highly skewed class distribution generally predicts the more frequently occurring classes much more often than the infrequently occurr...
Gary M. Weiss, Kate McCarthy, Bibi Zabar
UAI
1996
15 years 5 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon
CORR
2010
Springer
163views Education» more  CORR 2010»
15 years 4 months ago
Distributed Principal Component Analysis for Wireless Sensor Networks
Abstract: The Principal Component Analysis (PCA) is a data dimensionality reduction technique well-suited for processing data from sensor networks. It can be applied to tasks like ...
Yann-Aël Le Borgne, Sylvain Raybaud, Gianluca...