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Publication
145views
6 years 3 months ago
POP: Person Re-Identification Post-Rank Optimisation
Owing to visual ambiguities and disparities, person re-identification methods inevitably produce suboptimal rank-list, which still requires exhaustive human eyeballing to identify ...
C. Liu, C. C. Loy, S. Gong, G. Wang

Publication
129views
6 years 3 months ago
Video Synopsis by Heterogeneous Multi-Source Correlation
Generating coherent synopsis for surveillance video stream remains a formidable challenge due to the ambiguity and uncertainty inherent to visual observations. In contrast to exist...
X. Zhu, C. C. Loy, and S. Gong

Publication
335views
7 years 3 months ago
Person Re-Identification: What Features are Important?
State-of-the-art person re-identi cation methods seek robust person matching through combining various feature types. Often, these features are implicitly assigned with a single ve...
Chunxiao Liu, Shaogang Gong, Chen Change Loy, Xing...
ICIP
2010
IEEE
8 years 11 months ago
Building Emerging Pattern (EP) Random forest for recognition
The Random forest classifier comes to be the working horse for visual recognition community. It predicts the class label of an input data by aggregating the votes of multiple tree...
Liang Wang, Yizhou Wang, Debin Zhao
TDP
2010
124views more  TDP 2010»
8 years 11 months ago
Random Forests for Generating Partially Synthetic, Categorical Data
Abstract. Several national statistical agencies are now releasing partially synthetic, public use microdata. These comprise the units in the original database with sensitive or ide...
Gregory Caiola, Jerome P. Reiter
BMCBI
2007
147views more  BMCBI 2007»
9 years 1 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...
BMCBI
2006
198views more  BMCBI 2006»
9 years 1 months ago
Gene selection and classification of microarray data using random forest
Background: Selection of relevant genes for sample classification is a common task in most gene expression studies, where researchers try to identify the smallest possible set of ...
Ramón Díaz-Uriarte, Sara Alvarez de ...
BMCBI
2010
150views more  BMCBI 2010»
9 years 1 months ago
Automatic structure classification of small proteins using random forest
Background: Random forest, an ensemble based supervised machine learning algorithm, is used to predict the SCOP structural classification for a target structure, based on the simi...
Pooja Jain, Jonathan D. Hirst
CATA
2009
9 years 2 months ago
Nearest Shrunken Centroid as Feature Selection of Microarray Data
The nearest shrunken centroid classifier uses shrunken centroids as prototypes for each class and test samples are classified to belong to the class whose shrunken centroid is nea...
Myungsook Klassen, Nyunsu Kim
ECAI
2008
Springer
9 years 3 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
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