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27篇 您的检索式：作者名="Tom Mitchell"

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	题名	作者	年代	出处	被引量
1	Text Classification from Labeled and Unlabeled Documents using EM显示文摘	Kamal Nigam Andrew Kachites Mccallum Sebastian Thrun Tom Mitchell	2000	Machine Learning (-)2000，，2：	2
2	Text Classification from Labeled and Unlabeled Documents using EM显示文摘	Kamal Nigam Andrew Kachites Mccallum Sebastian Thrun Tom Mitchell	2000	Machine Learning (-)2000，，2：	1
3	Text Classification from Labeled and Unlabeled Documents using EM显示文摘	Kamal Nigam Andrew Kachites Mccallum Sebastian Thrun Tom Mitchell	2000	Machine Learning (-)2000，，2：	1
4	Text Classification from Labeled and Unlabeled Documents using EM显示文摘	Kamal Nigam Andrew Kachites Mccallum Sebastian Thrun Tom Mitchell	2000	Machine Learning (-)2000，，2：	1
5	Learning to decode cognitive states from brain Images显示文摘	Tom Mitchell	2004	Machine learning2004，，：	1
6	Text Classification from Labeled and Unlabeled Documents using EM显示文摘	Kamal Nigam Andrew Kachites Mccallum Sebastian Thrun Tom Mitchell	2000	Machine Learning (-)2000，，2：	1
7	Explanation-based generalization: A unifying view显示文摘	Tom M. Mitchell Richard M. Keller Smadar T. Kedar-Cabelli	1986	Machine Learning1986，，1：	1
8	Text Classification from Labeled and Unlabeled Docu- nlents using EM显示文摘	Kamal Nigam Andrew Kachites Mccallum Sebastian Thrun Tom Mitchell	2000	Machine Learning2000，，23：	1
9	Text Classification from Labeled and Unlabeled Documents using EM显示文摘	Kamal Nigam Andrew Kachites Mccallum Sebastian Thrun Tom Mitchell	2000	Machine Learning (-)2000，，2：	1
10	Text Classification from Labeled and Unlabeled Documents using EM显示文摘	Kamal Nigam Andrew Kachites Mccallum Sebastian Thrun Tom Mitchell	2000	Machine Learning (-)2000，，2：	1
11	Explanation-based generalization: A unifying view显示文摘	Tom M. Mitchell Richard M. Keller Smadar T. Kedar-Cabelli	1986	Machine Learning1986，，1：	1
12	“AI未来三大趋势:感知、自然语言处理、手机技术扩散”显示文摘AI在过去十年里面取得了令人叹为观止的成就。十年前,计算机基本上是看不见、听不见的,但是现在计算机能听得到我们的声音,看得到我们的图像,几乎能够像人一样,有时候能够比人更厉害。那么接下来的趋势是什么?一是感知能力:计算机将达到甚至超越人的感知能力,譬如在医疗领域,计算机能够比皮肤科专家更迅速准确地诊断人类皮肤癌。二是人类自然语言处理能力:研究人员已经训练了大量文本给计算机,让计算机充分理解语意。	Tom Mitchell	2019	上海信息化2019，0，9：	1
13	Text Classification from Labeled and Unlabeled Documents using EM显示文摘	Kamal Nigam Andrew Kachites Mccallum Sebastian Thrun Tom Mitchell	2000	Machine Learning (-)2000，，2：	1
14	Learning to Decode Cognitive States from Brain Images显示文摘	Tom M. Mitchell Rebecca Hutchinson Radu S. Niculescu Francisco Pereira Xuerui Wang Marcel Just Sharlene Newman	2004	Machine Learning (-)2004，，1：	1
15	E-assessment for learn-ing the potential of short-answer free-text questions withtailored feedback显示文摘	JORDAN Sally MITCHELl Tom	2009	British Journal of Educational Tech-nology2009，40，2：	1
16	Text Classification from Labeled and Unlabeled Documents using EM显示文摘	Kamal Nigam Andrew Kachites Mccallum Sebastian Thrun Tom Mitchell	2000	Machine Learning (-)2000，，2：	1
17	Why People Stay: Using Job Embeddedness to Predict Voluntary Turnover显示文摘	Mitchell T R Hoi tom B C Sablynski C J	2001	Academy of Management Journal2001，44，6：	1
18	Does machine learning really work? 显示文摘	TOM M Mitchell	1997	AI Magazine1997，18，3：	1
19	Cyclodextrin-based liquid chromatographic enantiomeric separation of chiral dihydrofurocoumarins, an emerging class of medicinal compounds显示文摘	Douglas D. Schumacher Clifford R. Mitchell Tom L. Xiao Roman V. Rozhkov Richard C. Larock Daniel W. Armstrong	2003	Journal of Chromatography A2003，，1：	1
20	Text Classification from Labeled and Unlabeled Documents using EM显示文摘	Kamal Nigam Andrew Kachites Mccallum Sebastian Thrun Tom Mitchell	2000	Machine Learning (-)2000，，2：	1

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