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PRL
2007
166views more  PRL 2007»
13 years 9 months ago
Boosted Landmarks of Contextual Descriptors and Forest-ECOC: A novel framework to detect and classify objects in cluttered scene
In this paper, we present a novel methodology to detect and recognize objects in cluttered scenes by proposing boosted contextual descriptors of landmarks in a framework of multi-...
Sergio Escalera, Oriol Pujol, Petia Radeva
ACMSE
2008
ACM
13 years 11 months ago
Mining frequent sequential patterns with first-occurrence forests
In this paper, a new pattern-growth algorithm is presented to mine frequent sequential patterns using First-Occurrence Forests (FOF). This algorithm uses a simple list of pointers...
Erich Allen Peterson, Peiyi Tang
BMCBI
2007
147views more  BMCBI 2007»
13 years 9 months ago
Bias in random forest variable importance measures: Illustrations, sources and a solution
Variable importance measures for random forests have been receiving increased attention as a means of variable selection in many classification tasks in bioinformatics and relate...
Carolin Strobl, Anne-Laure Boulesteix, Achim Zeile...
CORR
2010
Springer
96views Education» more  CORR 2010»
13 years 10 months ago
Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates
The problem of learning forest-structured discrete graphical models from i.i.d. samples is considered. An algorithm based on pruning of the Chow-Liu tree through adaptive threshol...
Vincent Y. F. Tan, Animashree Anandkumar, Alan S. ...
ECAI
2008
Springer
13 years 11 months ago
MTForest: Ensemble Decision Trees based on Multi-Task Learning
Many ensemble methods, such as Bagging, Boosting, Random Forest, etc, have been proposed and widely used in real world applications. Some of them are better than others on noisefre...
Qing Wang, Liang Zhang, Mingmin Chi, Jiankui Guo