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115
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ICML
2000
IEEE
16 years 4 months ago
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness
Most machine learning algorithms are lazy: they extract from the training set the minimum information needed to predict its labels. Unfortunately, this often leads to models that ...
Joseph O'Sullivan, John Langford, Rich Caruana, Av...
ICDM
2008
IEEE
97views Data Mining» more  ICDM 2008»
15 years 10 months ago
Semi-supervised Learning from General Unlabeled Data
We consider the problem of Semi-supervised Learning (SSL) from general unlabeled data, which may contain irrelevant samples. Within the binary setting, our model manages to better...
Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. L...
117
Voted
ISCAS
2007
IEEE
149views Hardware» more  ISCAS 2007»
15 years 10 months ago
Low-Power Circuits for Brain-Machine Interfaces
—This paper presents work on ultra-low-power circuits for brain–machine interfaces with applications for paralysis prosthetics, stroke, Parkinson’s disease, epilepsy, prosthe...
Rahul Sarpeshkar, Woradorn Wattanapanitch, Benjami...
265
Voted
KDD
2008
ACM
159views Data Mining» more  KDD 2008»
16 years 4 months ago
Semi-supervised learning with data calibration for long-term time series forecasting
Many time series prediction methods have focused on single step or short term prediction problems due to the inherent difficulty in controlling the propagation of errors from one ...
Haibin Cheng, Pang-Ning Tan
169
Voted
SDM
2011
SIAM
256views Data Mining» more  SDM 2011»
14 years 6 months ago
Temporal Structure Learning for Clustering Massive Data Streams in Real-Time
This paper describes one of the first attempts to model the temporal structure of massive data streams in real-time using data stream clustering. Recently, many data stream clust...
Michael Hahsler, Margaret H. Dunham