A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability),New

A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability),New

In Stock
SKU: DADAX0387946187
UPC: 9780387946184
Brand: Springer
Condition: New
$144.77
Quantity
Add to wishlist
Add to compare

Sold by Ergodebooks, an authorized reseller.

Returns accepted within 30 days | support@ergodebooks.com

Verified
Shipping Information
  • Free Standard Shipping — United States only
  • Processing Time: 1–3 business days
  • Estimated Delivery: 3–5 business days after dispatch
  • Double-boxed, fully insured & discreetly packaged
  • Tracking number sent via email once dispatched
  • Orders over $250 require signature upon delivery. Taxes calculated at checkout.
Returns & Refund

Returns accepted within 30 days of delivery.

Damaged or Defective Item

Free return shipping + replacement or full refund

Wrong Item Received

Free return shipping + replacement or full refund

Change of Mind

Return shipping at customer's expense · 25% restocking fee applies

All returns require a Return Authorization (RA) number before sending.

To initiate a return, contact us:

support@ergodebooks.com +1 (281) 738-1050
View Full Return & Refund Policy
Payment Option
Payment Methods

Help

If you have any questions, you are always welcome to contact us. We'll get back to you as soon as possible, withing 24 hours on weekdays.

Customer service

All questions about your order, return and delivery must be sent to our customer service team by e-mail at yourstore@yourdomain.com

Sale & Press

If you are interested in selling our products, need more information about our brand or wish to make a collaboration, please contact us at press@yourdomain.com

Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. The aim of this book is to provide a selfcontained account of probabilistic analysis of these approaches. The book includes a discussion of distance measures, nonparametric methods based on kernels or nearest neighbors, VapnikChervonenkis theory, epsilon entropy, parametric classification, error estimation, free classifiers, and neural networks. Wherever possible, distributionfree properties and inequalities are derived. A substantial portion of the results or the analysis is new. Over 430 problems and exercises complement the material.

⚠️ WARNING (California Proposition 65):

This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.

For more information, please visit www.P65Warnings.ca.gov.

Recently Viewed