Results 21 to 30 of about 433,523 (290)
Statistical Mechanics of Time Domain Ensemble Learning [PDF]
, 2006 Conventional ensemble learning combines students in the space domain. On the other hand, in this paper we combine students in the time domain and call it time domain ensemble learning. In this paper, we analyze the generalization performance of time domain ensemble learning in the framework of online learning using a statistical mechanical method.
arxiv +1 more source
Retarded Learning: Rigorous Results from Statistical Mechanics [PDF]
Phys. Rev. Lett. 86(10), pp. 2174-2177, 2001, 2001 We study learning of probability distributions characterized by an unknown symmetry direction. Based on an entropic performance measure and the variational method of statistical mechanics we develop exact upper and lower bounds on the scaled critical number of examples below which learning of the direction is impossible. The asymptotic tightness of the
arxiv +1 more source
Molecular Oncology, EarlyView.Large multidimensional digital images of cancer tissue are becoming prolific, but many challenges exist to automatically extract relevant information from them using computational tools. We describe publicly available resources that have been developed jointly by expert and non‐expert computational biologists working together during a virtual hackathon
Sandhya Prabhakaran +16 more
wiley +1 more source
Note on universal algorithms for learning theory [PDF]
Applicationes Mathematicae (Warsaw) 34 (2007), no. 1, 47 - 52, 2018 We propose the general way of study the universal estimator for the regression problem in learning theory considered in "Universal algorithms for learning theory Part I: piecewise constant functions" and "Universal algorithms for learning theory Part II: piecewise constant functions" written by Binev, P., Cohen, A., Dahmen, W., DeVore, R., Temlyakov, V.
arxiv +1 more source
Molecular Oncology, EarlyView.Prostate cancer is a leading malignancy with significant clinical heterogeneity in men. An 11‐gene signature derived from dysregulated epithelial cell markers effectively predicted biochemical recurrence‐free survival in patients who underwent radical surgery or radiotherapy.
Zhuofan Mou, Lorna W. Harries
wiley +1 more source
Optimization of the Asymptotic Property of Mutual Learning Involving an Integration Mechanism of Ensemble Learning [PDF]
, 2007 We propose an optimization method of mutual learning which converges into the identical state of optimum ensemble learning within the framework of on-line learning, and have analyzed its asymptotic property through the statistical mechanics method.The proposed model consists of two learning steps: two students independently learn from a teacher, and ...
arxiv +1 more source
Online Learning for Statistical Machine Translation [PDF]
Computational Linguistics, 2016 We present online learning techniques for statistical machine translation (SMT). The availability of large training data sets that grow constantly over time is becoming more and more frequent in the field of SMT—for example, in the context of translation agencies or the daily translation of government proceedings.
openaire +3 more sources
Machine learning, statistical learning and the future of biological research in psychiatry [PDF]
Psychological Medicine, 2016 Psychiatric research has entered the age of ‘Big Data’. Datasets now routinely involve thousands of heterogeneous variables, including clinical, neuroimaging, genomic, proteomic, transcriptomic and other ‘omic’ measures. The analysis of these datasets is challenging, especially when the number of measurements exceeds the number of individuals, and may ...
Iniesta, R.; Stahl, D.; McGuffin, P.
openaire +6 more sources
Exploration of heterogeneity and recurrence signatures in hepatocellular carcinoma
Molecular Oncology, EarlyView.This study leveraged public datasets and integrative bioinformatic analysis to dissect malignant cell heterogeneity between relapsed and primary HCC, focusing on intercellular communication, differentiation status, metabolic activity, and transcriptomic profiles.
Wen‐Jing Wu +15 more
wiley +1 more source
Global solutions to folded concave penalized nonconvex learning [PDF]
Annals of Statistics 2016, Vol. 44, No. 2, 629-659, 2016 This paper is concerned with solving nonconvex learning problems with folded concave penalty. Despite that their global solutions entail desirable statistical properties, they lack optimization techniques that guarantee global optimality in a general setting.
arxiv +1 more source