| (One intermediate revision by the same user not shown) | |||
| Line 1: | Line 1: | ||
<center><font size= 4> | <center><font size= 4> | ||
| − | Questions and comments | + | Questions and comments for [[Estimation_Using_Nearest_Neighbor|Nearest Neighbor Method]]. |
</font size> | </font size> | ||
A [https://www.projectrhea.org/learning/slectures.php slecture] by Sang Ho Yoon | A [https://www.projectrhea.org/learning/slectures.php slecture] by Sang Ho Yoon | ||
| − | |||
| − | |||
</center> | </center> | ||
---- | ---- | ||
| Line 11: | Line 9: | ||
If you have any questions, comments, etc. please post them on this page. | If you have any questions, comments, etc. please post them on this page. | ||
| − | |||
| − | |||
---- | ---- | ||
| Line 19: | Line 15: | ||
* (This slecture is reviewed by Sujin Jang) In this lecture, the author introduces basic principles of NN. Then various aspects of errors and distance metrics related to NN are further described. Overall, it is well written and easy to follow the main concept. Also, proper examples are provided to aid the understanding. Especially, I like section 4 Metric Type & Application. The effect of different distance metrics in boundary shapes is well represented in Figure 3. Also, the examples on body posture and shape recognition are briefly explained but effective to get a practical sense how NN can be implemented. I recommend the author to put little bit more details on those examples or to provide more practical examples with details. Also, comparison with other classification methods will be another future direction of this lecture. | * (This slecture is reviewed by Sujin Jang) In this lecture, the author introduces basic principles of NN. Then various aspects of errors and distance metrics related to NN are further described. Overall, it is well written and easy to follow the main concept. Also, proper examples are provided to aid the understanding. Especially, I like section 4 Metric Type & Application. The effect of different distance metrics in boundary shapes is well represented in Figure 3. Also, the examples on body posture and shape recognition are briefly explained but effective to get a practical sense how NN can be implemented. I recommend the author to put little bit more details on those examples or to provide more practical examples with details. Also, comparison with other classification methods will be another future direction of this lecture. | ||
| + | ---- | ||
| + | Back to [[NNM|Nearest Neighbor Method]]. | ||
| − | |||
[[2014_Spring_ECE_662_Boutin|Back to ECE 662 S14 course wiki]] | [[2014_Spring_ECE_662_Boutin|Back to ECE 662 S14 course wiki]] | ||
[[ECE662|Back to ECE 662 course page]] | [[ECE662|Back to ECE 662 course page]] | ||
Latest revision as of 18:12, 12 May 2014
Questions and comments for Nearest Neighbor Method.
A slecture by Sang Ho Yoon
If you have any questions, comments, etc. please post them on this page.
Questions and Comments
- (This slecture is reviewed by Sujin Jang) In this lecture, the author introduces basic principles of NN. Then various aspects of errors and distance metrics related to NN are further described. Overall, it is well written and easy to follow the main concept. Also, proper examples are provided to aid the understanding. Especially, I like section 4 Metric Type & Application. The effect of different distance metrics in boundary shapes is well represented in Figure 3. Also, the examples on body posture and shape recognition are briefly explained but effective to get a practical sense how NN can be implemented. I recommend the author to put little bit more details on those examples or to provide more practical examples with details. Also, comparison with other classification methods will be another future direction of this lecture.
Back to Nearest Neighbor Method.
