How Long to Read New Developments in Unsupervised Outlier Detection

By Xiaochun Wang

How Long Does it Take to Read New Developments in Unsupervised Outlier Detection?

It takes the average reader 4 hours and 44 minutes to read New Developments in Unsupervised Outlier Detection by Xiaochun Wang

Assuming a reading speed of 250 words per minute. Learn more

Description

This book enriches unsupervised outlier detection research by proposing several new distance-based and density-based outlier scores in a k-nearest neighbors’ setting. The respective chapters highlight the latest developments in k-nearest neighbor-based outlier detection research and cover such topics as our present understanding of unsupervised outlier detection in general; distance-based and density-based outlier detection in particular; and the applications of the latest findings to boundary point detection and novel object detection. The book also offers a new perspective on bridging the...

How long is New Developments in Unsupervised Outlier Detection?

New Developments in Unsupervised Outlier Detection by Xiaochun Wang is 277 pages long, and a total of 71,189 words.

This makes it 93% the length of the average book. It also has 87% more words than the average book.

How Long Does it Take to Read New Developments in Unsupervised Outlier Detection Aloud?

The average oral reading speed is 183 words per minute. This means it takes 6 hours and 29 minutes to read New Developments in Unsupervised Outlier Detection aloud.

What Reading Level is New Developments in Unsupervised Outlier Detection?

New Developments in Unsupervised Outlier Detection is suitable for students ages 12 and up.

Note that there may be other factors that effect this rating besides length that are not factored in on this page. This may include things like complex language or sensitive topics not suitable for students of certain ages.

When deciding what to show young students always use your best judgement and consult a professional.

Where Can I Buy New Developments in Unsupervised Outlier Detection?

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