Results 11 to 20 of about 23,402,464 (90)
An introduction to statistical learning with applications in R
Statistical Theory and Related Fields, 2021 The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
Fariha Sohil +2 more
semanticscholar +1 more source
Machine learning and statistical models for predicting indoor air quality.
Indoor Air: International Journal of Indoor Environment and Health, 2019 Indoor air quality (IAQ), as determined by the concentrations of indoor air pollutants, can be predicted using either physically-based mechanistic models or statistical models that are driven by measured data.
Wenjuan Wei +5 more
semanticscholar +1 more source
Visualising statistical models using dynamic nomograms
PLoS ONE, 2019 Translational Statistics proposes to promote the use of Statistics within research and improve the communication of statistical findings in an accurate and accessible manner to diverse audiences.
Amirhossein Jalali +3 more
semanticscholar +1 more source
Head-Driven Statistical Models for Natural Language Parsing
International Conference on Computational Logic, 2003 This article describes three statistical models for natural language parsing. The models extend methods from probabilistic context-free grammars to lexicalized grammars, leading to approaches in which a parse tree is represented as the sequence of ...
M. Collins
semanticscholar +1 more source
Active Learning with Statistical Models [PDF]
Neural Information Processing Systems, 1996 For many types of learners one can compute the statistically "optimal" way to select data. We review how these techniques have been used with feedforward neural networks [MacKay, 1992; Cohn, 1994].
D. Cohn +2 more
semanticscholar +1 more source
Mathematical Foundations of Infinite-Dimensional Statistical Models
, 2015 1. Nonparametric statistical models 2. Gaussian processes 3. Empirical processes 4. Function spaces and approximation theory 5. Linear nonparametric estimators 6. The minimax paradigm 7. Likelihood-based procedures 8. Adaptive inference.
E. Giné, Richard Nickl
semanticscholar +1 more source
Programming With Models: Writing Statistical Algorithms for General Model Structures With NIMBLE [PDF]
, 2015 We describe NIMBLE, a system for programming statistical algorithms for general model structures within R. NIMBLE is designed to meet three challenges: flexible model specification, a language for programming algorithms that can use different models, and
P. de Valpine +5 more
semanticscholar +1 more source
Learning statistical models of phenotypes using noisy labeled training data
J. Am. Medical Informatics Assoc., 2016 OBJECTIVE Traditionally, patient groups with a phenotype are selected through rule-based definitions whose creation and validation are time-consuming. Machine learning approaches to electronic phenotyping are limited by the paucity of labeled training ...
V. Agarwal +8 more
semanticscholar +1 more source
, 2017 We compare predictions of a simple process-based crop model (Soltani and Sinclair ), a simple statistical model (Schlenker and Roberts ), and a combination of both models to actual maize yields on a large, representative sample of farmer-managed fields ...
M. Roberts +4 more
semanticscholar +1 more source
Scientific Reports, 2015 This research presents the results of the GIS-based statistical models for generation of landslide susceptibility mapping using geographic information system (GIS) and remote-sensing data for Cameron Highlands area in Malaysia.
H. Shahabi, M. Hashim
semanticscholar +1 more source