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» Infinite Ensemble Learning with Support Vector Machines
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KDD
2004
ACM
166views Data Mining» more  KDD 2004»
16 years 2 months ago
Predicting prostate cancer recurrence via maximizing the concordance index
In order to effectively use machine learning algorithms, e.g., neural networks, for the analysis of survival data, the correct treatment of censored data is crucial. The concordan...
Lian Yan, David Verbel, Olivier Saidi
ICADL
2005
Springer
137views Education» more  ICADL 2005»
15 years 7 months ago
A Collaborative Filtering Based Re-ranking Strategy for Search in Digital Libraries
Users of a digital book library system typically interact with the system to search for books by querying on the metadata describing the books or to search for information in the p...
U. Rohini, Vamshi Ambati
FGR
2002
IEEE
229views Biometrics» more  FGR 2002»
15 years 6 months ago
An Approach to Automatic Recognition of Spontaneous Facial Actions
We present ongoing work on a project for automatic recognition of spontaneous facial actions. Spontaneous facial expressions differ substantially from posed expressions, similar t...
Bjorn Braathen, Marian Stewart Bartlett, Gwen Litt...
BMCBI
2007
207views more  BMCBI 2007»
15 years 1 months ago
Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clus
Background: The four heterogeneous childhood cancers, neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma present a similar histology of small round blue cell...
Nikhil R. Pal, Kripamoy Aguan, Animesh Sharma, Shu...
IPM
2008
100views more  IPM 2008»
15 years 1 months ago
Query-level loss functions for information retrieval
Many machine learning technologies such as support vector machines, boosting, and neural networks have been applied to the ranking problem in information retrieval. However, since...
Tao Qin, Xu-Dong Zhang, Ming-Feng Tsai, De-Sheng W...