Machine Learning and Data Mining Annual Volume 2023
The interest within the academic community regarding AI has experienced exponential growth in recent years. Several key factors have contributed to this surge in interest. Firstly, the rapid advancements in AI technologies have showcased their potential to revolutionize various fields, such as healt...
Gespeichert in:
Format: | Buch |
---|---|
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | |
container_volume | |
description | The interest within the academic community regarding AI has experienced exponential growth in recent years. Several key factors have contributed to this surge in interest. Firstly, the rapid advancements in AI technologies have showcased their potential to revolutionize various fields, such as healthcare, finance, and transportation, sparking curiosity and enthusiasm among researchers and scholars. Secondly, the availability of vast amounts of data and computing power has enabled academics to delve deeper into AI research, exploring complex algorithms and models to tackle real-world problems. Additionally, the interdisciplinary nature of AI has encouraged collaboration among experts from diverse fields like computer science, neuroscience, psychology, and ethics, fostering a rich exchange of ideas and approaches. With contributions from a diverse group of authors, this book offers a multifaceted perspective on machine learning and data mining. Whether you’re an experienced researcher or a newcomer, this collection is an essential resource for staying at the forefront of these dynamic and influential disciplines. |
doi_str_mv | 10.5772/intechopen.113978 |
format | Book |
fullrecord | <record><control><sourceid>oapen</sourceid><recordid>TN_cdi_oapen_doabooks_135427</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>135427</sourcerecordid><originalsourceid>FETCH-LOGICAL-g1002-3fe1e7dd849db8db5f5e05d5e788ef6b92cf833d8c854ee75def67dac2cb55223</originalsourceid><addsrcrecordid>eNotj8tOwzAQRS0BElXJByCx8A-kjD2e2llW5VGkVGyAbeXHpA0EG5H2_4mAzb06d3GkK8S1ggVZq2_7fOR4KF-cF0phY92ZqKYER6AMKVLnYqaXiLUygJeiGsd3AEAES42ZCdz6eOgzy5b9d-7zXvqc5J0_erntf3mV88kP8q0Mp0-WGjReiYvODyNX_z0Xrw_3L-tN3T4_Pq1Xbb1XALrGjhXblJxpUnApUEcMlIitc9wtQ6Nj5xCTi44Ms6U0rTb5qGMg0hrn4ubPW_x0b5eKD6V8jDuFZLTFH2euRtg</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>book</recordtype></control><display><type>book</type><title>Machine Learning and Data Mining Annual Volume 2023</title><source>InTech Open Access Books</source><source>DOAB: Directory of Open Access Books</source><contributor>Antonio Aceves-Fernández, Marco</contributor><creatorcontrib>Antonio Aceves-Fernández, Marco</creatorcontrib><description>The interest within the academic community regarding AI has experienced exponential growth in recent years. Several key factors have contributed to this surge in interest. Firstly, the rapid advancements in AI technologies have showcased their potential to revolutionize various fields, such as healthcare, finance, and transportation, sparking curiosity and enthusiasm among researchers and scholars. Secondly, the availability of vast amounts of data and computing power has enabled academics to delve deeper into AI research, exploring complex algorithms and models to tackle real-world problems. Additionally, the interdisciplinary nature of AI has encouraged collaboration among experts from diverse fields like computer science, neuroscience, psychology, and ethics, fostering a rich exchange of ideas and approaches. With contributions from a diverse group of authors, this book offers a multifaceted perspective on machine learning and data mining. Whether you’re an experienced researcher or a newcomer, this collection is an essential resource for staying at the forefront of these dynamic and influential disciplines.</description><identifier>ISSN: 2633-1403</identifier><identifier>ISBN: 9780850145151</identifier><identifier>ISBN: 9780850145137</identifier><identifier>ISBN: 0850145139</identifier><identifier>ISBN: 0850145155</identifier><identifier>ISBN: 9780850145144</identifier><identifier>ISBN: 0850145147</identifier><identifier>DOI: 10.5772/intechopen.113978</identifier><language>eng</language><publisher>IntechOpen</publisher><subject>Artificial intelligence ; Computer science ; Computing and Information Technology ; Machine learning</subject><creationdate>2023</creationdate><tpages>150</tpages><format>150</format><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><relation>Artificial Intelligence</relation></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>306,776,780,782,27902,55285</link.rule.ids></links><search><contributor>Antonio Aceves-Fernández, Marco</contributor><title>Machine Learning and Data Mining Annual Volume 2023</title><description>The interest within the academic community regarding AI has experienced exponential growth in recent years. Several key factors have contributed to this surge in interest. Firstly, the rapid advancements in AI technologies have showcased their potential to revolutionize various fields, such as healthcare, finance, and transportation, sparking curiosity and enthusiasm among researchers and scholars. Secondly, the availability of vast amounts of data and computing power has enabled academics to delve deeper into AI research, exploring complex algorithms and models to tackle real-world problems. Additionally, the interdisciplinary nature of AI has encouraged collaboration among experts from diverse fields like computer science, neuroscience, psychology, and ethics, fostering a rich exchange of ideas and approaches. With contributions from a diverse group of authors, this book offers a multifaceted perspective on machine learning and data mining. Whether you’re an experienced researcher or a newcomer, this collection is an essential resource for staying at the forefront of these dynamic and influential disciplines.</description><subject>Artificial intelligence</subject><subject>Computer science</subject><subject>Computing and Information Technology</subject><subject>Machine learning</subject><issn>2633-1403</issn><isbn>9780850145151</isbn><isbn>9780850145137</isbn><isbn>0850145139</isbn><isbn>0850145155</isbn><isbn>9780850145144</isbn><isbn>0850145147</isbn><fulltext>true</fulltext><rsrctype>book</rsrctype><creationdate>2023</creationdate><recordtype>book</recordtype><sourceid>V1H</sourceid><recordid>eNotj8tOwzAQRS0BElXJByCx8A-kjD2e2llW5VGkVGyAbeXHpA0EG5H2_4mAzb06d3GkK8S1ggVZq2_7fOR4KF-cF0phY92ZqKYER6AMKVLnYqaXiLUygJeiGsd3AEAES42ZCdz6eOgzy5b9d-7zXvqc5J0_erntf3mV88kP8q0Mp0-WGjReiYvODyNX_z0Xrw_3L-tN3T4_Pq1Xbb1XALrGjhXblJxpUnApUEcMlIitc9wtQ6Nj5xCTi44Ms6U0rTb5qGMg0hrn4ubPW_x0b5eKD6V8jDuFZLTFH2euRtg</recordid><startdate>2023</startdate><enddate>2023</enddate><general>IntechOpen</general><scope>V1H</scope></search><sort><creationdate>2023</creationdate><title>Machine Learning and Data Mining Annual Volume 2023</title></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-g1002-3fe1e7dd849db8db5f5e05d5e788ef6b92cf833d8c854ee75def67dac2cb55223</frbrgroupid><rsrctype>books</rsrctype><prefilter>books</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Artificial intelligence</topic><topic>Computer science</topic><topic>Computing and Information Technology</topic><topic>Machine learning</topic><toplevel>online_resources</toplevel><collection>DOAB: Directory of Open Access Books</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Antonio Aceves-Fernández, Marco</au><format>book</format><genre>book</genre><ristype>BOOK</ristype><btitle>Machine Learning and Data Mining Annual Volume 2023</btitle><seriestitle>Artificial Intelligence</seriestitle><date>2023</date><risdate>2023</risdate><issn>2633-1403</issn><isbn>9780850145151</isbn><isbn>9780850145137</isbn><isbn>0850145139</isbn><isbn>0850145155</isbn><isbn>9780850145144</isbn><isbn>0850145147</isbn><abstract>The interest within the academic community regarding AI has experienced exponential growth in recent years. Several key factors have contributed to this surge in interest. Firstly, the rapid advancements in AI technologies have showcased their potential to revolutionize various fields, such as healthcare, finance, and transportation, sparking curiosity and enthusiasm among researchers and scholars. Secondly, the availability of vast amounts of data and computing power has enabled academics to delve deeper into AI research, exploring complex algorithms and models to tackle real-world problems. Additionally, the interdisciplinary nature of AI has encouraged collaboration among experts from diverse fields like computer science, neuroscience, psychology, and ethics, fostering a rich exchange of ideas and approaches. With contributions from a diverse group of authors, this book offers a multifaceted perspective on machine learning and data mining. Whether you’re an experienced researcher or a newcomer, this collection is an essential resource for staying at the forefront of these dynamic and influential disciplines.</abstract><pub>IntechOpen</pub><doi>10.5772/intechopen.113978</doi><tpages>150</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2633-1403 |
ispartof | |
issn | 2633-1403 |
language | eng |
recordid | cdi_oapen_doabooks_135427 |
source | InTech Open Access Books; DOAB: Directory of Open Access Books |
subjects | Artificial intelligence Computer science Computing and Information Technology Machine learning |
title | Machine Learning and Data Mining Annual Volume 2023 |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-06T08%3A41%3A31IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-oapen&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=book&rft.btitle=Machine%20Learning%20and%20Data%20Mining%20Annual%20Volume%202023&rft.au=Antonio%20Aceves-Fern%C3%A1ndez,%20Marco&rft.date=2023&rft.issn=2633-1403&rft.isbn=9780850145151&rft.isbn_list=9780850145137&rft.isbn_list=0850145139&rft.isbn_list=0850145155&rft.isbn_list=9780850145144&rft.isbn_list=0850145147&rft_id=info:doi/10.5772/intechopen.113978&rft_dat=%3Coapen%3E135427%3C/oapen%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |