Shankar Vembu
Shankar Vembu
Unknown affiliation
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Cited by
Cited by
Chemical gas sensor drift compensation using classifier ensembles
A Vergara, S Vembu, T Ayhan, MA Ryan, ML Homer, R Huerta
Sensors and Actuators B: Chemical 166, 320-329, 2012
PhyloWGS: reconstructing subclonal composition and evolution from whole-genome sequencing of tumors
AG Deshwar, S Vembu, CK Yung, GH Jang, L Stein, Q Morris
Genome biology 16 (1), 1-20, 2015
Inferring clonal evolution of tumors from single nucleotide somatic mutations
W Jiao, S Vembu, AG Deshwar, L Stein, Q Morris
BMC bioinformatics 15 (1), 1-16, 2014
DOLCE ergo SUMO: On foundational and domain models in the SmartWeb Integrated Ontology (SWIntO)
D Oberle, A Ankolekar, P Hitzler, P Cimiano, M Sintek, M Kiesel, ...
Journal of Web Semantics 5 (3), 156-174, 2007
Label ranking algorithms: A survey
S Vembu, T Gärtner
Preference learning, 45-64, 2010
Separation of vocals from polyphonic audio recordings
S Vembu, S Baumann
Proceedings of the 6th International Conference of Music Information Retrieval, 2005
Beam search algorithms for multilabel learning
A Kumar, S Vembu, AK Menon, C Elkan
Machine learning 92 (1), 65-89, 2013
Predicting accurate probabilities with a ranking loss
A Menon, X Jiang, S Vembu, C Elkan, L Ohno-Machado
Proceedings of the 29th International Conference on Machine Learning, 2012
Towards bridging the semantic gap in multimedia annotation and retrieval
S Vembu, M Kiesel, M Sintek, S Baumann
Proceedings of the 1st International Workshop on Semantic Web Annotations …, 2006
RNAcompete-S: Combined RNA sequence/structure preferences for RNA binding proteins derived from a single-step in vitro selection
KB Cook, S Vembu, KCH Ha, H Zheng, KU Laverty, TR Hughes, D Ray, ...
Methods 126, 18-28, 2017
Using the electronic medical record to identify patients at high risk for frequent emergency department visits and high system costs
DW Frost, S Vembu, J Wang, K Tu, Q Morris, HB Abrams
The American journal of medicine 130 (5), 601. e17-601. e22, 2017
A self-organizing map based knowledge discovery for music recommendation systems
S Vembu, S Baumann
Computer music modeling and retrieval, Lecture Notes in Computer Science …, 2005
Inhibition in multiclass classification
R Huerta, S Vembu, JM Amigó, T Nowotny, C Elkan
Neural computation 24 (9), 2473-2507, 2012
On time series features and kernels for machine olfaction
S Vembu, A Vergara, MK Muezzinoglu, R Huerta
Sensors and Actuators B: Chemical 174, 535-546, 2012
Towards a socio-cultural compatibility of MIR systems
S Baumann, T Pohle, V Shankar
Proceedings of the 5th International Conference of Music Information Retrieval, 2004
On structured output training: Hard cases and an efficient alternative
T Gärtner, S Vembu
Machine learning 76 (2-3), 227-242, 2009
Dynamical SVM for time series classification
R Huerta, S Vembu, MK Muezzinoglu, A Vergara
Proceedings of the Joint 34th DAGM and 36th OAGM Symposium, 2012
Probabilistic structured predictors
S Vembu, T Gärtner, M Boley
Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, 2009
Using error decay prediction to overcome practical issues of deep active learning for named entity recognition
HS Chang, S Vembu, S Mohan, R Uppaal, A McCallum
Machine Learning 109 (9), 1749-1778, 2020
Interactive learning from multiple noisy labels
S Vembu, S Zilles
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2016
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