Presidential Young Investigator Award (Computer and         Information Science)
{'Code': '05020000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'IIS', 'LongName': 'Div Of Information & Intelligent Systems'}}
{'SignBlockName': 'Larry H. Reeker', 'PO_EMAI': None, 'PO_PHON': None}
A machine learning program is a program that improves its               performance over time, usually by acquiring new knowledge about                 the tasks it is performing. This research seeks to improve our                  understanding of both the foundations and the practical aspects                 of building machine learning programs. The focus is in three                    directions:  (a) finding ways by which learning programs can use                the knowledge they already have to learn additional knowledge,                  (b) understanding the role of active experimentation in aiding                  the learning process, and (c) analyzing the constraints that                    different representation languages place on the learning process.               To make progress in each of these areas, controlled experiments                 are conducted to compare different learning methods,                            experimentation strategies, and representation languages.                               The importance of this research lies in the fact that the               production and debugging of software systems and expert systems                 is the principal cost of using computers - hardware and personnel               costs are small in comparison.  Machine learning techniques hold                the potential for significantly enhancing the way computers can                 be used. Rather than programming the computer, step by step, we                 would like to give the computer an approximate, incomplete, and                 inefficient procedure for a desired task.  This approximate,                    incomplete, and inefficient program could then be debugged,                     perfected, and made more efficient automatically, through the use               of machine learning techniques.  Computers could be efficiently                 taught, rather than laboriously programmed.
{'FirstName': 'Thomas', 'LastName': 'Dietterich', 'PI_MID_INIT': 'G', 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Thomas G Dietterich', 'EmailAddress': 'tgd@cs.orst.edu', 'NSF_ID': '000190533', 'StartDate': '10/01/1987', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Oregon State University', 'CityName': 'CORVALLIS', 'ZipCode': '973318655', 'PhoneNumber': '5417374933', 'StreetAddress': '1500 SW JEFFERSON AVE', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Oregon', 'StateCode': 'OR', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'OR04', 'ORG_UEI_NUM': 'MZ4DYXE1SL98', 'ORG_LGL_BUS_NAME': 'OREGON STATE UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}
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[{'Code': '1128', 'Text': 'Population & Community Ecology'}, {'Code': '6835', 'Text': 'KNOWLEDGE & DATABASE SYSTEMS'}, {'Code': '6856', 'Text': 'ARTIFICIAL INTELL & COGNIT SCI'}]
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