Zobrazeno 1 - 10
of 151
pro vyhledávání: '"Tin Kam Ho"'
Publikováno v:
IEEE Signal Processing Magazine. 39:96-106
Publikováno v:
IEEE Signal Processing Magazine. 39:5-7
Publikováno v:
Pattern Recognition Letters. 131:46-48
Autor:
Tin Kam Ho, Yunfeng Zhang, Neil Mallinar, Robert L. Yates, Ayush Gupta, Rachel K. E. Bellamy, Ugrani Rajendra G, Abhishek Shah, Q. Vera Liao, Manikandan Gurusankar, Chris Desmarais, Blake McGregor
Publikováno v:
AAAI
Many conversational agents in the market today follow a standard bot development framework which requires training intent classifiers to recognize user input. The need to create a proper set of training examples is often the bottleneck in the develop
Autor:
Tin Kam Ho, Jiangang Hao
Publikováno v:
Journal of Educational and Behavioral Statistics. 44:348-361
Machine learning is a popular topic in data analysis and modeling. Many different machine learning algorithms have been developed and implemented in a variety of programming languages over the past 20 years. In this article, we first provide an overv
Publikováno v:
AAAI
Real-world text classification tasks often require many labeled training examples that are expensive to obtain. Recent advancements in machine teaching, specifically the data programming paradigm, facilitate the creation of training data sets quickly
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::43edd770c07f95970a9895354a087260
Publikováno v:
IEEE Transactions on Smart Grid. :1-11
With the gradual deployment of smart meters in many cities around the world, new opportunities arise in reducing energy usage and improving consumers’ information and control on their electricity consumption. Central to the provision of these newer
Publikováno v:
IEEE Transactions on Network and Service Management. 12:467-482
The U.S. National Academies of Science's Board on Science, Technology and Economic Policy estimates that the Internet and voice-over-IP (VoIP) communications infrastructure generates 10% of U.S. economic growth. As market forces move increasingly tow
Publikováno v:
ICPR
Application of machine learning to new and unfamiliar domains calls for increasing automation in choosing a learning algorithm suitable for the data arising from each domain. Meta-learning could address this need since it has been largely used in the
Autor:
Xiao Bai, Edwin R. Hancock, Tin Kam Ho, Richard C. Wilson, Battista Biggio, Antonio Robles-Kelly
This book constitutes the proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2018, held in Beijing, China, in August 2018.The 49 papers presented in this volume were carefully