Results 41 to 50 of about 414,531 (51)
Elephant Search with Deep Learning for Microarray Data Analysis
, 2017 Even though there is a plethora of research in Microarray gene expression data analysis, still, it poses challenges for researchers to effectively and efficiently analyze the large yet complex expression of genes.
Panda, Mrutyunjaya
core
Rethinking Administrative Law for Algorithmic Decision Making. [PDF]
Oxf J Leg Stud, 2022 Williams R.
europepmc +1 more source
Generalization Guarantees for a Binary Classification Framework for Two-Stage Multiple Kernel Learning [PDF]
arXiv, 2013 We present generalization bounds for the TS-MKL framework for two stage multiple kernel learning. We also present bounds for sparse kernel learning formulations within the TS-MKL framework.
arxiv
Utility-based Dueling Bandits as a Partial Monitoring Game
, 2015 Partial monitoring is a generic framework for sequential decision-making with incomplete feedback. It encompasses a wide class of problems such as dueling bandits, learning with expect advice, dynamic pricing, dark pools, and label efficient prediction ...
Gajane, Pratik, Urvoy, Tanguy
core
Proceedings of the 29th International Conference on Machine Learning (ICML-12) [PDF]
arXiv, 2012 This is an index to the papers that appear in the Proceedings of the 29th International Conference on Machine Learning (ICML-12). The conference was held in Edinburgh, Scotland, June 27th - July 3rd, 2012.
arxiv
Distributed Multi-Task Learning with Shared Representation [PDF]
arXiv, 2016 We study the problem of distributed multi-task learning with shared representation, where each machine aims to learn a separate, but related, task in an unknown shared low-dimensional subspaces, i.e. when the predictor matrix has low rank. We consider a setting where each task is handled by a different machine, with samples for the task available ...
arxiv
Components of Machine Learning: Binding Bits and FLOPS [PDF]
arXiv, 2019 Many machine learning problems and methods are combinations of three components: data, hypothesis space and loss function. Different machine learning methods are obtained as combinations of different choices for the representation of data, hypothesis space and loss function.
arxiv
Spatial Transfer Learning with Simple MLP [PDF]
arXivFirst step to investigate the potential of transfer learning applied to the field of spatial ...
arxiv
A Note on Kaldi's PLDA Implementation [PDF]
arXiv, 2018 Some explanations to Kaldi's PLDA implementation to make formula derivation easier to catch.
arxiv