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Object Detection in Images by Components
(1999-08-11)
In this paper we present a component based person detection system that is capable of detecting frontal, rear and near side views of people, and partially occluded persons in cluttered scenes. The framework that is ...
Discriminative Gaussian Process Latent Variable Model for Classification
(2007-03-28)
Supervised learning is difficult with high dimensional input spacesand very small training sets, but accurate classification may bepossible if the data lie on a low-dimensional manifold. GaussianProcess Latent Variable ...
Deep Learning without Poor Local Minima
(2016-05-23)
In this paper, we prove a conjecture published in 1989 and also partially address an open problem announced at the Conference on Learning Theory (COLT) 2015. For an expected loss function of a deep nonlinear neural network, ...
Towards Understanding Generalization via Analytical Learning Theory
(2018-10-01)
This paper introduces a novel measure-theoretic theory for machine learning
that does not require statistical assumptions. Based on this theory, a new
regularization method in deep learning is derived and shown to ...
Evaluation of Machine Learning Tools for Predicting Sand Production
(2021-03-12)
Data analytics has only recently picked the interest of the oil and gas industry as it has made data visualization much simpler, faster, and cost-effective. This is driven by the promising innovative techniques in developing ...
Probability of Wellbore Failure and its Prediction Using Machine Learning
(AUST, 2019-06-09)
Wellbore instability (WI) is one of the major challenges experienced during drilling operations costing the oil and gas industry over $1 billion yearly. During the drilling, borehole breakout and drilling induced fractures ...
Evaluation of Machine Learning Tools for Predicting Sand Production
(AUST, 2021-03-10)
Data analytics has only recently picked the interest of the oil and gas industry as it has made data visualization much simpler, faster, and cost-effective. This is driven by the promising innovative techniques in developing ...
Machine Learning Techniques for Malaria Incidence and Tuberculosis Prediction
(AUST, 2021-07-02)
This research proposes machine learning techniques to develop models that would facilitate decision-making in health informatics. It focuses on using efficient machine learning techniques to solve the pressing need in the ...