Advances in Machine Learning (Branch of Artificial Intelligence) /
Machine learning, a branch of artificial intelligence, is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. A learner can take advantage of examples (data) t...
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| Format: | text |
| Language: | eng |
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Delhi, India : World Technologies,
2012
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| Online Access: | http://repository.library.utm.my/2795 |
| _version_ | 1796762809774637056 |
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| author | Cooney, Margert author 640107 |
| author_facet | Cooney, Margert author 640107 |
| author_sort | Cooney, Margert author 640107 |
| collection | OCEAN |
| description | Machine learning, a branch of artificial intelligence, is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. A learner can take advantage of examples (data) to capture characteristics of interest of their unknown underlying probability distribution. Data can be seen as examples that illustrate relations between observed variables. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too large to be covered by the set of observed examples (training data). Hence the learner must generalize from the given examples, so as to be able to produce a useful output in new cases. Machine Learning, like all subjects in artificial intelligence, require cross-disciplinary proficiency in several areas, such as probability theory, statistics, pattern recognition, cognitive science, data mining, adaptive control, computational neuroscience and theoretical computer science. |
| first_indexed | 2024-03-05T16:46:21Z |
| format | text |
| id | KOHA-OAI-TEST:593644 |
| institution | Universiti Teknologi Malaysia - OCEAN |
| language | eng |
| last_indexed | 2024-03-05T16:46:21Z |
| publishDate | 2012 |
| publisher | Delhi, India : World Technologies, |
| record_format | dspace |
| spelling | KOHA-OAI-TEST:5936442022-11-07T14:21:12ZAdvances in Machine Learning (Branch of Artificial Intelligence) / Cooney, Margert author 640107 text Electronic books 631902 Delhi, India : World Technologies,2012©2012engMachine learning, a branch of artificial intelligence, is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. A learner can take advantage of examples (data) to capture characteristics of interest of their unknown underlying probability distribution. Data can be seen as examples that illustrate relations between observed variables. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too large to be covered by the set of observed examples (training data). Hence the learner must generalize from the given examples, so as to be able to produce a useful output in new cases. Machine Learning, like all subjects in artificial intelligence, require cross-disciplinary proficiency in several areas, such as probability theory, statistics, pattern recognition, cognitive science, data mining, adaptive control, computational neuroscience and theoretical computer science.Machine learning, a branch of artificial intelligence, is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. A learner can take advantage of examples (data) to capture characteristics of interest of their unknown underlying probability distribution. Data can be seen as examples that illustrate relations between observed variables. A major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data; the difficulty lies in the fact that the set of all possible behaviors given all possible inputs is too large to be covered by the set of observed examples (training data). Hence the learner must generalize from the given examples, so as to be able to produce a useful output in new cases. Machine Learning, like all subjects in artificial intelligence, require cross-disciplinary proficiency in several areas, such as probability theory, statistics, pattern recognition, cognitive science, data mining, adaptive control, computational neuroscience and theoretical computer science.Deep learning (Machine learning)Artificial intelligencehttp://repository.library.utm.my/2795URN:ISBN:9788132344773Remote access restricted to users with a valid UTM ID via VPN. |
| spellingShingle | Deep learning (Machine learning) Artificial intelligence Cooney, Margert author 640107 Advances in Machine Learning (Branch of Artificial Intelligence) / |
| title | Advances in Machine Learning (Branch of Artificial Intelligence) / |
| title_full | Advances in Machine Learning (Branch of Artificial Intelligence) / |
| title_fullStr | Advances in Machine Learning (Branch of Artificial Intelligence) / |
| title_full_unstemmed | Advances in Machine Learning (Branch of Artificial Intelligence) / |
| title_short | Advances in Machine Learning (Branch of Artificial Intelligence) / |
| title_sort | advances in machine learning branch of artificial intelligence |
| topic | Deep learning (Machine learning) Artificial intelligence |
| url | http://repository.library.utm.my/2795 |
| work_keys_str_mv | AT cooneymargertauthor640107 advancesinmachinelearningbranchofartificialintelligence |