Join Amazon Prime and ship Two-Day for free and Overnight for $3.99. Already a member? Sign in.
Frequently Bought Together
Customers Who Bought This Item Also Bought
Similarity Search: The Metric Space Approach (Advances in Database Systems) by Pavel Zezula |
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) by Carl Edward Rasmussen |
by Gary Bradski
|
by Hanan Samet
|
Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher M. Bishop |
Editorial Reviews
Product Description
Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these methods on large data sets. This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic. It brings together contributions from researchers in theory of computation, machine learning, and computer vision with the goals of bridging the gaps between disciplines and presenting state-of-the-art methods for emerging applications.
The contributors focus on the importance of designing algorithms for NN search, and for the related classification, regression, and retrieval tasks, that remain efficient even as the number of points or the dimensionality of the data grows very large. The book begins with two theoretical chapters on computational geometry and then explores ways to make the NN approach practicable in machine learning applications where the dimensionality of the data and the size of the data sets make the naïve methods for NN search prohibitively expensive. The final chapters describe successful applications of an NN algorithm, locality-sensitive hashing (LSH), to vision tasks.
About the Author
Gregory Shakhnarovich is a Postdoctoral Research Associate in the Computer Science Department at Brown University
Trevor Darrell is Associate Professor and Head of the Vision Interface Group in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT.
Piotr Indyk is Associate Professor in the Theory of Computation Group in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT.
Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these methods on large data sets. This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic. It brings together contributions from researchers in theory of computation, machine learning, and computer vision with the goals of bridging the gaps between disciplines and presenting state-of-the-art methods for emerging applications.
The contributors focus on the importance of designing algorithms for NN search, and for the related classification, regression, and retrieval tasks, that remain efficient even as the number of points or the dimensionality of the data grows very large. The book begins with two theoretical chapters on computational geometry and then explores ways to make the NN approach practicable in machine learning applications where the dimensionality of the data and the size of the data sets make the naïve methods for NN search prohibitively expensive. The final chapters describe successful applications of an NN algorithm, locality-sensitive hashing (LSH), to vision tasks.
About the Author
Gregory Shakhnarovich is a Postdoctoral Research Associate in the Computer Science Department at Brown University
Trevor Darrell is Associate Professor and Head of the Vision Interface Group in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT.
Piotr Indyk is Associate Professor in the Theory of Computation Group in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT.
Product Details
Would you like to update product info or give feedback on images?
|
Tag this product(What's this?)Think of a tag as a keyword or label you consider is strongly related to this product.
Tags will help all customers organize and find favorite items. |
Help others find this product — tag it for Amazon search
No one has tagged this product for Amazon search yet. Why not be the first to suggest a search for which it should appear?
Sell a Digital Version of This Book in the Kindle Store
If you are a publisher or author and hold the digital rights to a book, you can sell a digital version of it in our Kindle Store.
Learn more
Customer Reviews
There are no customer reviews yet. Create your own review
| |||
Video reviews
|
 |
Customer Discussions
Beta (What's this?)
New! See all customer communities, and bookmark your communities to keep track of them.
|
This product's forum
(0 discussions)
Ask questions, Share opinions, Gain insight
Active discussions in related forums
|
Related forums
|
|||||||||||||||||||||||||||||||||||||||||
Product Information from the Amapedia Community
Beta (What's this?)Be the first person to add an article about this item at Amapedia.com.
› See featured Amapedia.com articles
Listmania!
|
|
So You'd Like to...
Look for Similar Items by Category
- Books > Computers & Internet > Computer Science > Artificial Intelligence > Machine Learning
- Books > Computers & Internet > Computer Science > Artificial Intelligence > Theory of Computing
- Books > Computers & Internet > Programming > Algorithms
- Books > Health, Mind & Body > Disorders & Diseases > Eye Problems
- Books > Health, Mind & Body > Psychology & Counseling
Great Deals on Magazines
Visit our huge selection of magazine subscriptions often to see the latest special offers and bonuses. Check out magazines like
The New Yorker,
Wired, and
Vanity Fair. |
Best Books of 2008 |
Dive into Summer Reading |
Quieter, More Comfortable Living |
|||||||
|
Your Recent History
(What's this?)|
You have no recently viewed items or searches.
After viewing product detail pages or search results, look here to find an easy way to navigate back to pages you are interested in. Look to the right column to find helpful suggestions for your shopping session. |
Continue shopping: Top Sellers
| ||||
| ||
| ||
| ||
| ||
| ||
