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» A Boosting Algorithm for Regression
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163
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TIP
2010
155views more  TIP 2010»
15 years 2 months ago
Laplacian Regularized D-Optimal Design for Active Learning and Its Application to Image Retrieval
—In increasingly many cases of interest in computer vision and pattern recognition, one is often confronted with the situation where data size is very large. Usually, the labels ...
Xiaofei He
158
Voted
DIS
2006
Springer
15 years 5 months ago
Change Detection with Kalman Filter and CUSUM
Knowledge discovery systems are constrained by three main limited resources: time, memory and sample size. Sample size is traditionally the dominant limitation, but in many present...
Milton Severo, João Gama
124
Voted
NIPS
2003
15 years 5 months ago
Online Passive-Aggressive Algorithms
We present a family of margin based online learning algorithms for various prediction tasks. In particular we derive and analyze algorithms for binary and multiclass categorizatio...
Shai Shalev-Shwartz, Koby Crammer, Ofer Dekel, Yor...
EUSFLAT
2007
126views Fuzzy Logic» more  EUSFLAT 2007»
15 years 5 months ago
Selecting the Optimal Rule Set Using a Bacterial Evolutionary Algorithm
In many regression learning algorithms for fuzzy rule bases it is not possible to define the error measure to be optimized freely. A possible alternative is the usage of global o...
Mario Drobics, János Botzheim, Klaus-Peter ...
ICMLA
2004
15 years 5 months ago
A new landmarker generation algorithm based on correlativity
Landmarking is a recent and promising metalearning strategy, which defines meta-features that are themselves efficient learning algorithms. However, the choice of landmarkers is m...
Daren Ler, Irena Koprinska, Sanjay Chawla