Machine Learning Methods with Noisy, Incomplete or Small Datasets
In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, i...
Saved in:
| HerausgeberIn: | |
|---|---|
| Sonstige: | |
| Year of Publication: | 2021 |
| Language: | English |
| Physical Description: | 1 electronic resource (316 p.) |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
993545488904498 |
|---|---|
| ctrlnum |
(CKB)5400000000042633 (oapen)https://directory.doabooks.org/handle/20.500.12854/76309 (EXLCZ)995400000000042633 |
| collection |
bib_alma |
| record_format |
marc |
| spelling |
Solé-Casals, Jordi edt Machine Learning Methods with Noisy, Incomplete or Small Datasets Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 1 electronic resource (316 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios. English Information technology industries bicssc open contours similarly shaped fish species Discrete Cosine Transform (DCT) Discrete Fourier Transform (DFT) Extreme Learning Machines (ELM) feature engineering small data-sets optimization machine learning preprocessing image generation weighted interpolation map binarization single sample per person root canal measurement multifrequency impedance data augmentation neural network functional magnetic resonance imaging independent component analysis deep learning recurrent neural network functional connectivity episodic memory small sample learning feature selection noise elimination space consistency label correlations empirical mode decomposition sparse representations tensor decomposition tensor completion machine translation pairwise evaluation educational data small datasets noisy datasets smart building Internet of Things (IoT) Markov Chain Monte Carlo (MCMC) ontology graph model Artificial Neural Network Discriminant Analysis dengue feature extraction sound event detection non-negative matrix factorization ultrasound images shadow detection shadow estimation auto-encoders semi-supervised learning prediction feature importance feature elimination hierarchical clustering Parkinson’s disease few-shot learning permutation-variable importance topological data analysis persistent entropy support-vector machine data science intelligent decision support social vulnerability gender-gap digital-gap COVID19 policy-making support artificial intelligence imperfect dataset 3-0365-1288-8 3-0365-1287-X Sun, Zhe edt Caiafa, Cesar F. edt Marti-Puig, Pere edt Tanaka, Toshihisa edt Solé-Casals, Jordi oth Sun, Zhe oth Caiafa, Cesar F. oth Marti-Puig, Pere oth Tanaka, Toshihisa oth |
| language |
English |
| format |
eBook |
| author2 |
Sun, Zhe Caiafa, Cesar F. Marti-Puig, Pere Tanaka, Toshihisa Solé-Casals, Jordi Sun, Zhe Caiafa, Cesar F. Marti-Puig, Pere Tanaka, Toshihisa |
| author_facet |
Sun, Zhe Caiafa, Cesar F. Marti-Puig, Pere Tanaka, Toshihisa Solé-Casals, Jordi Sun, Zhe Caiafa, Cesar F. Marti-Puig, Pere Tanaka, Toshihisa |
| author2_variant |
j s c jsc z s zs c f c cf cfc p m p pmp t t tt |
| author2_role |
HerausgeberIn HerausgeberIn HerausgeberIn HerausgeberIn Sonstige Sonstige Sonstige Sonstige Sonstige |
| title |
Machine Learning Methods with Noisy, Incomplete or Small Datasets |
| spellingShingle |
Machine Learning Methods with Noisy, Incomplete or Small Datasets |
| title_full |
Machine Learning Methods with Noisy, Incomplete or Small Datasets |
| title_fullStr |
Machine Learning Methods with Noisy, Incomplete or Small Datasets |
| title_full_unstemmed |
Machine Learning Methods with Noisy, Incomplete or Small Datasets |
| title_auth |
Machine Learning Methods with Noisy, Incomplete or Small Datasets |
| title_new |
Machine Learning Methods with Noisy, Incomplete or Small Datasets |
| title_sort |
machine learning methods with noisy, incomplete or small datasets |
| publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
| publishDate |
2021 |
| physical |
1 electronic resource (316 p.) |
| isbn |
3-0365-1288-8 3-0365-1287-X |
| illustrated |
Not Illustrated |
| work_keys_str_mv |
AT solecasalsjordi machinelearningmethodswithnoisyincompleteorsmalldatasets AT sunzhe machinelearningmethodswithnoisyincompleteorsmalldatasets AT caiafacesarf machinelearningmethodswithnoisyincompleteorsmalldatasets AT martipuigpere machinelearningmethodswithnoisyincompleteorsmalldatasets AT tanakatoshihisa machinelearningmethodswithnoisyincompleteorsmalldatasets |
| status_str |
n |
| ids_txt_mv |
(CKB)5400000000042633 (oapen)https://directory.doabooks.org/handle/20.500.12854/76309 (EXLCZ)995400000000042633 |
| carrierType_str_mv |
cr |
| is_hierarchy_title |
Machine Learning Methods with Noisy, Incomplete or Small Datasets |
| author2_original_writing_str_mv |
noLinkedField noLinkedField noLinkedField noLinkedField noLinkedField noLinkedField noLinkedField noLinkedField noLinkedField |
| _version_ |
1796648768177700864 |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>04632nam-a2201261z--4500</leader><controlfield tag="001">993545488904498</controlfield><controlfield tag="005">20231214133621.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr|mn|---annan</controlfield><controlfield tag="008">202201s2021 xx |||||o ||| 0|eng d</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(CKB)5400000000042633</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(oapen)https://directory.doabooks.org/handle/20.500.12854/76309</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EXLCZ)995400000000042633</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Solé-Casals, Jordi</subfield><subfield code="4">edt</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine Learning Methods with Noisy, Incomplete or Small Datasets</subfield></datafield><datafield tag="260" ind1=" " ind2=" "><subfield code="a">Basel, Switzerland</subfield><subfield code="b">MDPI - Multidisciplinary Digital Publishing Institute</subfield><subfield code="c">2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 electronic resource (316 p.)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios.</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">English</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Information technology industries</subfield><subfield code="2">bicssc</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">open contours</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">similarly shaped fish species</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Discrete Cosine Transform (DCT)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Discrete Fourier Transform (DFT)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Extreme Learning Machines (ELM)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">feature engineering</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">small data-sets</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">optimization</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">machine learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">preprocessing</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">image generation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">weighted interpolation map</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">binarization</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">single sample per person</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">root canal measurement</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multifrequency impedance</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">data augmentation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">neural network</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">functional magnetic resonance imaging</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">independent component analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">deep learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">recurrent neural network</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">functional connectivity</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">episodic memory</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">small sample learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">feature selection</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">noise elimination</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">space consistency</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">label correlations</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">empirical mode decomposition</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">sparse representations</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">tensor decomposition</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">tensor completion</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">machine translation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">pairwise evaluation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">educational data</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">small datasets</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">noisy datasets</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">smart building</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Internet of Things (IoT)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Markov Chain Monte Carlo (MCMC)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">ontology</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">graph model</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Artificial Neural Network</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Discriminant Analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">dengue</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">feature extraction</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">sound event detection</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">non-negative matrix factorization</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">ultrasound images</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">shadow detection</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">shadow estimation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">auto-encoders</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">semi-supervised learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">prediction</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">feature importance</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">feature elimination</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">hierarchical clustering</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Parkinson’s disease</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">few-shot learning</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">permutation-variable importance</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">topological data analysis</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">persistent entropy</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">support-vector machine</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">data science</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">intelligent decision support</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">social vulnerability</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">gender-gap</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">digital-gap</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">COVID19</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">policy-making support</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">artificial intelligence</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">imperfect dataset</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">3-0365-1288-8</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">3-0365-1287-X</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Zhe</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Caiafa, Cesar F.</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Marti-Puig, Pere</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tanaka, Toshihisa</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Solé-Casals, Jordi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Zhe</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Caiafa, Cesar F.</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Marti-Puig, Pere</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tanaka, Toshihisa</subfield><subfield code="4">oth</subfield></datafield><datafield tag="906" ind1=" " ind2=" "><subfield code="a">BOOK</subfield></datafield><datafield tag="ADM" ind1=" " ind2=" "><subfield code="b">2023-12-15 05:59:26 Europe/Vienna</subfield><subfield code="f">system</subfield><subfield code="c">marc21</subfield><subfield code="a">2022-04-04 09:22:53 Europe/Vienna</subfield><subfield code="g">false</subfield></datafield><datafield tag="AVE" ind1=" " ind2=" "><subfield code="i">DOAB Directory of Open Access Books</subfield><subfield code="P">DOAB Directory of Open Access Books</subfield><subfield code="x">https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5337929260004498&Force_direct=true</subfield><subfield code="Z">5337929260004498</subfield><subfield code="b">Available</subfield><subfield code="8">5337929260004498</subfield></datafield></record></collection> |