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137
Voted
KDD
2009
ACM
198views Data Mining» more  KDD 2009»
16 years 2 months ago
Heterogeneous source consensus learning via decision propagation and negotiation
Nowadays, enormous amounts of data are continuously generated not only in massive scale, but also from different, sometimes conflicting, views. Therefore, it is important to conso...
Jing Gao, Wei Fan, Yizhou Sun, Jiawei Han
115
Voted
KDD
2008
ACM
207views Data Mining» more  KDD 2008»
16 years 2 months ago
Active learning with direct query construction
Active learning may hold the key for solving the data scarcity problem in supervised learning, i.e., the lack of labeled data. Indeed, labeling data is a costly process, yet an ac...
Charles X. Ling, Jun Du
112
Voted
KDD
2006
ACM
155views Data Mining» more  KDD 2006»
16 years 2 months ago
Single-pass online learning: performance, voting schemes and online feature selection
To learn concepts over massive data streams, it is essential to design inference and learning methods that operate in real time with limited memory. Online learning methods such a...
Vitor R. Carvalho, William W. Cohen
KDD
2005
ACM
177views Data Mining» more  KDD 2005»
15 years 7 months ago
Combining partitions by probabilistic label aggregation
Data clustering represents an important tool in exploratory data analysis. The lack of objective criteria render model selection as well as the identification of robust solutions...
Tilman Lange, Joachim M. Buhmann
110
Voted
SIGMOD
2010
ACM
236views Database» more  SIGMOD 2010»
15 years 6 months ago
Differentially private aggregation of distributed time-series with transformation and encryption
We propose PASTE, the first differentially private aggregation algorithms for distributed time-series data that offer good practical utility without any trusted server. PASTE add...
Vibhor Rastogi, Suman Nath