Results 31 to 40 of about 434,301 (280)

Learning oncogenetic networks by reducing to mixed integer linear programming. [PDF]

PLoS ONE, 2013
Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events.
Hossein Shahrabi Farahani, Jens Lagergren   +1 more
doaj   +1 more source

Bayesian Inference in Estimation of Distribution Algorithms [PDF]

, 2007
Metaheuristics such as Estimation of Distribution Algorithms and the Cross-Entropy method use probabilistic modelling and inference to generate candidate solutions in optimization problems.
Gallagher, Marcus, Keith, Jonathan, Sofronov, George, Wood, Ian   +3 more
core   +3 more sources

STEM-Based Bayesian Computational Learning Model-BCLM for Effective Learning of Bayesian Statistics

IEEE Access
This work contributes to the comprehension of Bayes’ theorem inclusive Bayesian probabilities and Bayesian inferencing within the framework of STEM (Science, Technology, Engineering, Arts, and Mathematics) and cognitive learning w.r.t Bloom’
Ikram E. Khuda, Sadique Ahmad, Abdelhamied Ashraf Ateya   +2 more
doaj   +1 more source

A nonparametric Bayesian approach toward robot learning by demonstration

, 2012
In the past years, many authors have considered application of machine learning methodologies to effect robot learning by demonstration. Gaussian mixture regression (GMR) is one of the most successful methodologies used for this purpose.
Antoniak, Argall, Argall, Billard, Billard, Billard, Billard, Bishop, Blackwell, Blei, Celeux, Chandler, Chatzis, Demiris, Dimitrios Korkinof, Ferguson, Ghahramani, Jordan, Leroux, Lopes, Lopes, Muller, Myersand, Neal, Pearlmutter, Qi, Rasmussen, Schwarz, Sethuraman, Skoglund, Sotirios P. Chatzis, Ude, Vapnik, Walker, Yiannis Demiris, Zegers   +35 more
core   +1 more source

Second-Order Belief Hidden Markov Models [PDF]

, 2014
Hidden Markov Models (HMMs) are learning methods for pattern recognition. The probabilistic HMMs have been one of the most used techniques based on the Bayesian model.
A. Aregui, D. Dubois, E. Ramasso, F. Fayad, G. Zhou, H. Soubaras, J. Kupiec, L.M. Lee, L.R. Rabiner, M.E.Y. Boudaren, P. Lanchantin, P. Smets, P. Smets, P. Smets, R.R. Yager, S.M. Thede, T. Brants, W. Pieczynski   +17 more
core   +5 more sources

Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa

, 2018
This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing work by considering the targeted ...
Damianou, Andreas, Drake, Thomas, Maarek, Yoelle, Yang, Jie   +3 more
core   +1 more source

Bayesian Model-Agnostic Meta-Learning

, 2018
First two authors contributed equally. 15 pages with appendix including experimental details.
Kim, Taesup, Yoon, Jaesik, Dia, Ousmane, Kim, Sungwoong, Bengio, Yoshua, Ahn, Sungjin   +5 more
openaire   +2 more sources

Empirical Bayesian learning in AR graphical models [PDF]

Automatica, 2019
Automatica (accepted)
openaire   +3 more sources

Hyperparameter Estimation for Sparse Bayesian Learning Models

SIAM/ASA Journal on Uncertainty Quantification
Sparse Bayesian Learning (SBL) models are extensively used in signal processing and machine learning for promoting sparsity through hierarchical priors. The hyperparameters in SBL models are crucial for the model's performance, but they are often difficult to estimate due to the non-convexity and the high-dimensionality of the associated objective ...
Yu, Feng, Shen, Lixin, Song, Guohui
openaire   +3 more sources

Long‐Term Follow‐Up of Chemotherapy‐Associated Biological Aging in Women With Early Breast Cancer

Aging and Cancer, EarlyView.
Women threated with adjuvant chemotherapy for early breast cancer have sustained long‐term increase in p16INK4a,, a robust marker of cell senescence, suggesting a chemotherapy‐associated age acceleration. p16INK4a as well as other biomarkers may identify patients at greatest risk for senescence‐related diseases of aging.
Hyman B. Muss, Alexis C. Wardell, Allison M. Deal, Kirsten A. Nyrop, Allison Ross, Yara G. Abdou, Jeffrey D. Aldrich, Lisa A. Cary, E. Claire Dees, Katherine E. Reeder‐Hayes, Emily M. Ray, Natalia Mitin, Denis Tsygankov   +12 more
wiley   +1 more source