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|Acronym=ICMLA 2019 | |Acronym=ICMLA 2019 | ||
|Title=18th IEEE International Conference on Machine Learning and Applications | |Title=18th IEEE International Conference on Machine Learning and Applications | ||
| − | | | + | |Ordinal=18 |
| − | | | + | |In Event Series=Event Series:1bd18e81-39e2-4006-b161-ebc19436e379 |
| + | |Single Day Event=no | ||
| + | |Start Date=2019/12/16 | ||
| + | |End Date=2019/12/19 | ||
| + | |Event Status=as scheduled | ||
| + | |Event Mode=on site | ||
| + | |Venue=Boca Raton Marriott at Boca Center | ||
|City=Boca Raton | |City=Boca Raton | ||
| − | | | + | |Region=Florida |
|Country=Country:US | |Country=Country:US | ||
| − | | | + | |Academic Field=Applications; Artificial Intelligence; Computer Science; Machine Learning |
| − | | | + | |Official Website=https://www.icmla-conference.org/icmla19 |
| − | | | + | |Submission Link=https://www.icmla-conference.org/icmla19/howtosubmit.html |
| − | + | |Registration Link=https://www.icmla-conference.org/database/techbd/users/ | |
| − | | | + | |Type=Conference |
|Has coordinator=IEEE SMC, Association for Machine Learning and Application(AMLA) | |Has coordinator=IEEE SMC, Association for Machine Learning and Application(AMLA) | ||
|has general chair=Taghi M. Khoshgoftaar | |has general chair=Taghi M. Khoshgoftaar | ||
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|On site regular=650 | |On site regular=650 | ||
|Early bird reduced=575 | |Early bird reduced=575 | ||
| − | |||
|has Proceedings Bibliography=https://dblp1.uni-trier.de/db/conf/icmla/ | |has Proceedings Bibliography=https://dblp1.uni-trier.de/db/conf/icmla/ | ||
|pageCreator=User:Curator 70 | |pageCreator=User:Curator 70 | ||
|pageEditor=User:Curator 70 | |pageEditor=User:Curator 70 | ||
|contributionType=1 | |contributionType=1 | ||
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}} | }} | ||
| + | {{Event Deadline | ||
| + | |Notification Deadline=2019/10/07 | ||
| + | |Camera-Ready Deadline=2019/10/17 | ||
| + | |Tutorial Deadline=2019/05/01 | ||
| + | |Submission Deadline=2019/09/07 | ||
| + | }} | ||
| + | {{Organizer | ||
| + | |Contributor Type=organization | ||
| + | |Organization=Association for Machine Learning and Applications | ||
| + | }} | ||
| + | {{Organizer | ||
| + | |Contributor Type=organization | ||
| + | |Organization=IEEE Systems, Man, and Cybernetics Society, Institute of Electrical and Electronics Engineers | ||
| + | }} | ||
| + | {{Event Metric}} | ||
| + | {{S Event}} | ||
ICMLA 2019 aims to bring together researchers and practitioners to present their latest achievements and innovations in the area of machine learning (ML). | ICMLA 2019 aims to bring together researchers and practitioners to present their latest achievements and innovations in the area of machine learning (ML). | ||
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==Topics== | ==Topics== | ||
| − | * statistical learning | + | *statistical learning |
| − | * neural network learning | + | *neural network learning |
| − | * learning through fuzzy logic | + | *learning through fuzzy logic |
| − | * learning through evolution (evolutionary algorithms) | + | *learning through evolution (evolutionary algorithms) |
| − | * reinforcement learning | + | *reinforcement learning |
| − | * multi-strategy learning | + | *multi-strategy learning |
| − | * cooperative learning | + | *cooperative learning |
| − | * planning and learning | + | *planning and learning |
| − | * multi-agent learning | + | *multi-agent learning |
| − | * online and incremental learning | + | *online and incremental learning |
| − | * scalability of learning algorithms | + | *scalability of learning algorithms |
| − | * inductive learning | + | *inductive learning |
| − | * inductive logic programming | + | *inductive logic programming |
| − | * Bayesian networks | + | *Bayesian networks |
| − | * support vector machines | + | *support vector machines |
| − | * case-based reasoning | + | *case-based reasoning |
| − | * machine learning for bioinformatics and computational biology | + | *machine learning for bioinformatics and computational biology |
| − | * multi-lingual knowledge acquisition and representation | + | *multi-lingual knowledge acquisition and representation |
| − | * grammatical inference | + | *grammatical inference |
| − | * knowledge acquisition and learning | + | *knowledge acquisition and learning |
| − | * knowledge discovery in databases | + | *knowledge discovery in databases |
| − | * knowledge intensive learning | + | *knowledge intensive learning |
| − | * knowledge representation and reasoning | + | *knowledge representation and reasoning |
| − | * machine learning and information retrieval | + | *machine learning and information retrieval |
| − | * machine learning for web navigation and mining | + | *machine learning for web navigation and mining |
| − | * learning through mobile data mining | + | *learning through mobile data mining |
| − | * text and multimedia mining through machine learning | + | *text and multimedia mining through machine learning |
| − | * distributed and parallel learning algorithms and applications | + | *distributed and parallel learning algorithms and applications |
| − | * feature extraction and classification | + | *feature extraction and classification |
| − | * theories and models for plausible reasoning | + | *theories and models for plausible reasoning |
| − | * computational learning theory | + | *computational learning theory |
| − | * cognitive modeling | + | *cognitive modeling |
| − | * hybrid learning algorithms | + | *hybrid learning algorithms |
==Applications of machine learning in== | ==Applications of machine learning in== | ||
| − | * medicine, health, bioinformatics and systems biology | + | *medicine, health, bioinformatics and systems biology |
| − | * industrial and engineering applications | + | *industrial and engineering applications |
| − | * security applications | + | *security applications |
| − | * smart cities | + | *smart cities |
| − | * game playing and problem solving | + | *game playing and problem solving |
| − | * intelligent virtual environments | + | *intelligent virtual environments |
| − | * economics, business and forecasting applications, etc | + | *economics, business and forecasting applications, etc |
==Submissions== | ==Submissions== | ||
Sep 7, 2019 | Sep 7, 2019 | ||
Latest revision as of 12:17, 11 October 2023
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Venue
Boca Raton Marriott at Boca Center, Boca Raton, Florida, United States of America
ICMLA 2019 aims to bring together researchers and practitioners to present their latest achievements and innovations in the area of machine learning (ML).
The conference provides a leading international forum for the dissemination of original research in ML, with emphasis on applications as well as novel algorithms and systems. Following the success of previous ICMLA conferences, the conference aims to attract researchers and application developers from a wide range of ML related areas, and the recent emergence of Big Data processing brings an urgent need for machine learning to address these new challenges. The conference will cover both machine learning theoretical research and its applications. Contributions describing machine learning techniques applied to real-world problems and interdisciplinary research involving machine learning, in fields like medicine, biology, industry, manufacturing, security, education, virtual environments, games, are especially encouraged.
Topics
- statistical learning
- neural network learning
- learning through fuzzy logic
- learning through evolution (evolutionary algorithms)
- reinforcement learning
- multi-strategy learning
- cooperative learning
- planning and learning
- multi-agent learning
- online and incremental learning
- scalability of learning algorithms
- inductive learning
- inductive logic programming
- Bayesian networks
- support vector machines
- case-based reasoning
- machine learning for bioinformatics and computational biology
- multi-lingual knowledge acquisition and representation
- grammatical inference
- knowledge acquisition and learning
- knowledge discovery in databases
- knowledge intensive learning
- knowledge representation and reasoning
- machine learning and information retrieval
- machine learning for web navigation and mining
- learning through mobile data mining
- text and multimedia mining through machine learning
- distributed and parallel learning algorithms and applications
- feature extraction and classification
- theories and models for plausible reasoning
- computational learning theory
- cognitive modeling
- hybrid learning algorithms
Applications of machine learning in
- medicine, health, bioinformatics and systems biology
- industrial and engineering applications
- security applications
- smart cities
- game playing and problem solving
- intelligent virtual environments
- economics, business and forecasting applications, etc
Submissions
Sep 7, 2019
