Lin2008discovering question 1 by lebo
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CSCI 6966 Advanced Semantic Web (Fall 2008)
- syllabus, announcements, presentations
- Lesson 1, Lesson 2, Lesson 3, Lesson 4, Lesson 5, Lesson 6,
- Lesson 7, Lesson 8, Lesson 9, Lesson 10, Lesson 11, Lesson 12, Lesson 13
A Question from Tim Lebo about lin2008discovering:
The authors make a very good point regarding the "ill defined" nature of using probabilistic measures for a deterministic graph structure. The two Random Experiments that they propose are unique, intuitive, and probabilistically sound methods for obtaining probabilistic measures. But from the time that they propose the method, they do not discuss the methods' computational expense until the last paragraph of the paper, "an important future direction is to improve the scalability of the system. What is most expensive is the computation of feature values, since it requires the system to count a potentially large number of paths."
- How long should the Random Experiments be reasonably run to obtain characterize the input while trading off the time required to do so?
- Answer:
Facts about Lin2008discovering question 1 by leboRDF feed
| A | Question + |
| About | Lin2008discovering + |
| Author | Tim Lebo + |
| Question asked | The authors make a very good point regardi … The authors make a very good point regarding the "ill defined" nature of using probabilistic measures for a deterministic graph structure. The two Random Experiments that they propose are unique, intuitive, and probabilistically sound methods for obtaining probabilistic measures. But from the time that they propose the method, they do not discuss the methods' computational expense until the last paragraph of the paper, "an important future direction is to improve the scalability of the system. What is most expensive is the computation of feature values, since it requires the system to count a potentially large number of paths."
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| Question asked by | Tim Lebo + |
| Question for the Presentation | Medha Journal Presentation + |
| Text | The authors make a very good point regardi … The authors make a very good point regarding the "ill defined" nature of using probabilistic measures for a deterministic graph structure. The two Random Experiments that they propose are unique, intuitive, and probabilistically sound methods for obtaining probabilistic measures. But from the time that they propose the method, they do not discuss the methods' computational expense until the last paragraph of the paper, "an important future direction is to improve the scalability of the system. What is most expensive is the computation of feature values, since it requires the system to count a potentially large number of paths."
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