Automatic selection method for machine learning in cloud computing environment

A cloud computing environment and machine learning technology, applied in computing, computing models, instruments, etc., can solve problems such as the inconvenience of machine learning modeling, achieve high-efficiency computing capabilities and transparency, and lower the threshold for use

Active Publication Date: 2010-07-21
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

By using the cloud computing platform, the problem of inconvenient machine learning modeling is solved, and a convenient and

Method used

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  • Automatic selection method for machine learning in cloud computing environment
  • Automatic selection method for machine learning in cloud computing environment
  • Automatic selection method for machine learning in cloud computing environment

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Embodiment Construction

[0156] The invention is a machine learning autonomous selection method based on cloud computing environment. By using the cloud computing platform, users do not need to build an operating environment for machine learning, do not need to select machine learning algorithms, and do not need to adjust complicated machine learning functions and their accompanying parameters. They only need to upload sample data through the Web, and it will be automatically intelligent. The establishment of machine learning mathematical models that meet practical problems. The invention frees the use of machine learning from the shackles of the environment, takes advantage of the cloud computing platform, makes machine learning modeling transparent to users, and reduces the threshold for using machine learning to the greatest extent. It solves the shortcomings of unpredictability in modeling selection, manual experience in parameter adjustment, and difficulty for ordinary users in the actual applica...

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Abstract

The invention relates to an automatic selection method for machine learning in cloud computer environment. By using a cloud computing platform, a user can automatically and intelligently build a machine learning mathematic model which meets actual problems without building the operation environment of machine learning, selecting a machine to learn algorithm and even adjusting complicated machine learning functions and accompanying parameters but only using a Web method to upload sample data. Through the method, the use of machine learning is free from the environmental constraints and displays the advantages of the cloud computing platform, so that the machine learning model building is transparent to the user, so as to best reduce the use threshold of machine learning. The automatic selection method for machine learning in cloud computing environment solves the disadvantages of the unpredictability of model building selection, the manual experience of parameter adjustment, the difficulties of common users and the like when machine learning is applied in actual life.

Description

technical field [0001] The invention is a machine learning autonomous selection method based on cloud computing environment. By using the cloud computing platform, users do not need to build a machine learning operating environment, learn machine learning algorithms, or adjust complicated machine learning functions and their parameters. They only need to upload training data using the Web under the cloud computing platform. And predict the test data, and determine the scope of use, expected domain and other little information, you can get a variety of machine learning models and specific descriptions needed to solve practical problems. Background technique [0002] Machine learning is another important application following the application of expert systems and artificial intelligence, and it is also a core research topic of artificial intelligence. Its purpose is to enable computers to simulate or experiment with human learning behaviors, thereby acquiring knowledge or ski...

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Application Information

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IPC IPC(8): G06N99/00
Inventor 王汝传孔强任勋益付雄邓松易侃杨明慧蒋凌云邓勇
Owner NANJING UNIV OF POSTS & TELECOMM
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