Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Bryan Kisiel"'
Autor:
William W. Cohen, J. Welling, Kathryn Mazaitis, Ndapandula Nakashole, Bryan Kisiel, Andrew Carlson, T. Mohamed, M. Greaves, Bhavana Dalvi, Tom M. Mitchell, Mehdi Samadi, Abulhair Saparov, Justin Betteridge, Jayant Krishnamurthy, Estevam R. Hruschka, Partha Pratim Talukdar, Ni Lao, Matt Gardner, Abhinav Gupta, Xinlei Chen, Bishan Yang, Emmanouil Antonios Platanios, Alan Ritter, Richard Wang, Derry Tanti Wijaya, Burr Settles
Publikováno v:
IndraStra Global.
Whereas people learn many different types of knowledge from diverse experiences over many years, and become better learners over time, most current machine learning systems are much more narrow, learning just a single function or data model based on
Autor:
Andrew Carlson, Justin Betteridge, Bryan Kisiel, Burr Settles, Estevam Hruschka, Tom Mitchell
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 24:1306-1313
We consider here the problem of building a never-ending language learner; that is, an intelligent computer agent that runs forever and that each day must (1) extract, or read, information from the web to populate a growing structured knowledge base,
Autor:
Tom Mitchell, William Cohen, Estevam Hruschka, Partha Talukdar, Justin Betteridge, Andrew Carlson, Bhavana Dalvi Mishra, Matthew Gardner, Bryan Kisiel, Jayant Krishnamurthy, Ni Lao, Kathryn Mazaitis, Thahir Mohamed, Ndapa Nakashole, Emmanouil Platanios, Alan Ritter, Mehdi Samadi, Burr Settles, Richard Wang, Derry Wijaya, Abhinav Gupta, Xinlei Chen, Abulhair Saparov, Malcolm Greaves, Joel Welling
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 29
Whereas people learn many different types of knowledge from diverse experiences over many years, most current machine learning systems acquire just a single function or data model from just a single data set. We propose a never-ending learning paradi
Publikováno v:
Proceedings of the 2009 SIAM International Conference on Data Mining.
Autor:
Chad Cumby, Bryan Kisiel, Katharina Probst, Henry Shu, Yiming Yang, Rayid Ghani, Abhimanyu Lad
Publikováno v:
ICEIS (2)
In many practical applications, multiple interrelated tasks must be accomplished sequentially through user interaction with retrieval, classification and recommendation systems. The ordering of the tasks may have a significant impact on the overall u
Publikováno v:
SIGIR
This paper examines a new approach to information distillation over temporally ordered documents, and proposes a novel evaluation scheme for such a framework. It combines the strengths of and extends beyond conventional adaptive filtering, novelty de
Publikováno v:
SIGIR
This paper reports a cross-benchmark evaluation of regularized logistic regression (LR) and incremental Rocchio for adaptive filtering. Using four corpora from the Topic Detection and Tracking (TDT) forum and the Text Retrieval Conferences (TREC) we
Autor:
Bryan Kisiel, Yiming Yang
Publikováno v:
CIKM
Adaptive information filtering is an open challenge in information retrieval. One of the tough issues is the optimization of decision thresholds over time, based on partial relevance feedback on the system-retrieved documents in chronological order.
Publikováno v:
SIGIR
Real-world applications of text categorization often require a system to deal with tens of thousands of categories defined over a large taxonomy. This paper addresses the problem with respect to a set of popular algorithms in text categorization, inc