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03446namaa2201033ui 4500 |
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20221228154426.0 |
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m o d |
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cr|mn|---annan |
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20220111s2021 xx |||||o ||| 0|eng d |
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|a books978-3-0365-2693-5
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|a 9783036526928
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|a 9783036526935
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|a oapen
|c oapen
|b eng
|d DE-2553
|e rda
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| 024 |
7 |
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|a 10.3390/books978-3-0365-2693-5
|c doi
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| 041 |
0 |
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|a eng
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|a dc
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| 072 |
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7 |
|a KNTX
|2 bicssc
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| 100 |
1 |
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|a Gocheva-Ilieva, Snezhana
|e editor
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| 264 |
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|b MDPI - Multidisciplinary Digital Publishing Institute,
|c 2021.
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| 700 |
1 |
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|a Gocheva-Ilieva, Snezhana
|e other
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| 245 |
1 |
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|a Statistical Data Modeling and Machine Learning with Applications
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| 300 |
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|a 1 online resource (184 pages).
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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0 |
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|a Open Access
|2 star
|f Unrestricted online access
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| 540 |
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|a Creative Commons
|f https://creativecommons.org/licenses/by/4.0/
|2 cc
|4 https://creativecommons.org/licenses/by/4.0/
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| 546 |
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|a English
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| 650 |
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7 |
|a Information technology industries
|2 bicssc
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| 653 |
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|a mathematical competency
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| 653 |
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|a assessment
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| 653 |
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|a machine learning
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| 653 |
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|a classification and regression tree
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| 653 |
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|a CART ensembles and bagging
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| 653 |
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|a ensemble model
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| 653 |
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|a multivariate adaptive regression splines
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| 653 |
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|a cross-validation
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| 653 |
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|a dam inflow prediction
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| 653 |
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|a long short-term memory
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| 653 |
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|a wavelet transform
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| 653 |
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|a input predictor selection
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| 653 |
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|a hyper-parameter optimization
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| 653 |
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|a brain-computer interface
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| 653 |
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|a EEG motor imagery
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| 653 |
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|a CNN-LSTM architectures
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| 653 |
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|a real-time motion imagery recognition
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| 653 |
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|a artificial neural networks
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| 653 |
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|a banking
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| 653 |
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|a hedonic prices
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| 653 |
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|a housing
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| 653 |
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|a quantile regression
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| 653 |
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|a data quality
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| 653 |
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|a citizen science
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| 653 |
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|a consensus models
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| 653 |
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|a clustering
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| 653 |
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|a Gower's interpolation formula
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| 653 |
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|a Gower's metric
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| 653 |
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|a mixed data
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|a multidimensional scaling
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|a classification
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| 653 |
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|a data-adaptive kernel functions
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|a image data
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|a multi-category classifier
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|a predictive models
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|a support vector machine
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|a stochastic gradient descent
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| 653 |
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|a damped Newton
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| 653 |
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|a convexity
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| 653 |
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|a METABRIC dataset
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| 653 |
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|a breast cancer subtyping
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|a deep forest
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| 653 |
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|a multi-omics data
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|a categorical data
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| 653 |
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|a similarity
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| 653 |
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|a feature selection
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| 653 |
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|a kernel density estimation
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| 653 |
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|a non-linear optimization
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| 653 |
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|a kernel clustering
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| 653 |
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|a n/a
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| 856 |
4 |
0 |
|a www.oapen.org
|u https://mdpi.com/books/pdfview/book/4733
|7 0
|z DOAB: download the publication
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| 856 |
4 |
0 |
|a www.oapen.org
|u https://directory.doabooks.org/handle/20.500.12854/77114
|7 0
|z DOAB: description of the publication
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| 590 |
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|a Online publication
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| 590 |
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|a ebookoa1222
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| 590 |
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|a doab
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| 942 |
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