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<font size="4">Questions and Comments for: '''[[KnnDensityEstimation|K-Nearest Neighbors Density Estimation]]''' </font> | <font size="4">Questions and Comments for: '''[[KnnDensityEstimation|K-Nearest Neighbors Density Estimation]]''' </font> | ||
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Jonathan Manring will review this slecture. | Jonathan Manring will review this slecture. | ||
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| − | *[Reviewed by Haiguang Wen] This slecture on K-Nearest Neighbors density estimation is well developed and easy to understand. The slecture focus on three parts. <br>• The concept and mathematical basis of KNN estimation method<br>• The application of KNN estimation method in classification.<br>• Computational complex of KNN<br>The theory explanation is very detailed and easy to follow. And at the end of each part, a brief summary or conclusion is given. But it would be better if there are simple examples with figures about applying KNN in classification and explain the computational complex of | + | *[Reviewed by Haiguang Wen] This slecture on K-Nearest Neighbors density estimation is well developed and easy to understand. The slecture focus on three parts. <br>• The concept and mathematical basis of KNN estimation method<br>• The application of KNN estimation method in classification.<br>• Computational complex of KNN<br>The theory explanation is very detailed and easy to follow. And at the end of each part, a brief summary or conclusion is given. But it would be better if there are simple examples with figures about applying KNN in classification and explain the computational complex of given examples. Overall speaking, it is a very good slecture.<br> |
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Latest revision as of 06:01, 3 May 2014
Questions and Comments for: K-Nearest Neighbors Density Estimation
A slecture by Raj Praveen Selvaraj
Please leave me comment below if you have any questions, if you notice any errors or if you would like to discuss a topic further.
Questions and Comments
Jonathan Manring will review this slecture.
- Additional Questions / Comments
- [Reviewed by Haiguang Wen] This slecture on K-Nearest Neighbors density estimation is well developed and easy to understand. The slecture focus on three parts.
• The concept and mathematical basis of KNN estimation method
• The application of KNN estimation method in classification.
• Computational complex of KNN
The theory explanation is very detailed and easy to follow. And at the end of each part, a brief summary or conclusion is given. But it would be better if there are simple examples with figures about applying KNN in classification and explain the computational complex of given examples. Overall speaking, it is a very good slecture.
