Machine Learning (Record no. 2354)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03835nas a22003975i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 1573-0565 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20260610082502.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 150723s||||||||xxuuu poo|||||| b|EN |d |
| 022 ## - INTERNATIONAL STANDARD SERIAL NUMBER | |
| International Standard Serial Number | 1573-0565 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1007/10994.1573-0565 |
| Source of number or code | doi |
| 210 10 - ABBREVIATED TITLE | |
| Abbreviated title | Mach Learn |
| 245 10 - TITLE STATEMENT | |
| Title | Machine Learning |
| Medium | [electronic resource] / |
| Statement of responsibility, etc. | edited by Michelangelo Ceci, Hendrik Blockeel. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | New York : |
| Name of producer, publisher, distributor, manufacturer | Springer US : |
| -- | Imprint: Springer. |
| 300 ## - online resource | |
| Other physical details | online resource. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Machine Learning is an international forum for research on computational approaches to learning. The journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems, including but not limited to: Learning Problems: Classification, regression, recognition, and prediction; Problem solving and planning; Reasoning and inference; Data mining; Web mining; Scientific discovery; Information retrieval; Natural language processing; Design and diagnosis; Vision and speech perception; Robotics and control; Combinatorial optimization; Game playing; Industrial, financial, and scientific applications of all kinds. Learning Methods: Supervised and unsupervised learning methods (including learning decision and regression trees, rules, connectionist networks, probabilistic networks and other statistical models, inductive logic programming, case-based methods, ensemble methods, clustering, etc.); Reinforcement learning; Evolution-based methods; Explanation-based learning; Analogical learning methods; Automated knowledge acquisition; Learning from instruction; Visualization of patterns in data; Learning in integrated architectures; Multistrategy learning; Multi-agent learning. Papers describe research on problems and methods, applications research, and issues of research methodology. Papers making claims about learning problems (e.g., inherent complexity) or methods (e.g., relative performance of alternative algorithms) provide solid support via empirical studies, theoretical analysis, or comparison to psychological phenomena. Applications papers show how to apply learning methods to solve important applications problems. Research methodology papers improve how machine learning research is conducted. All papers must state their contributions clearly and describe how the contributions are supported. All papers must describe the supporting evidence in ways that can be verified or replicated by other researchers. All papers must describe the learning component clearly, and must discuss assumptions regarding knowledge representation and the performance task. All papers must place their contribution clearly in the context of existing work in machine learning. Variations from these prototypes, such as comprehensive surveys of active research areas, critical reviews of existing work, and book reviews, will be considered provided they make a clear contribution to the field. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Control engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Robotics. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Automation. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Computer simulation. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Natural language processing (Computer science). |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Machine Learning. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Control, Robotics, Automation. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Computer Modelling. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Natural Language Processing (NLP). |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ceci, Michelangelo. |
| Relator term | editor. |
| Relationship | edt |
| -- | http://id.loc.gov/vocabulary/relators/edt |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Blockeel, Hendrik. |
| Relator term | editor. |
| Relationship | edt |
| -- | http://id.loc.gov/vocabulary/relators/edt |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Printed version: |
| International Standard Serial Number | 0885-6125 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://doi.org/10.1007/10994.1573-0565">https://doi.org/10.1007/10994.1573-0565</a> |
| Public note | Hybrid |
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