![](/Images/spacer.gif) |
![](/Images/menu_shadow.gif)
![](/Images/menu_shadow.gif)
|
![](/Images/spacer.gif) |
Item Details
Title:
|
LEARNING FROM DATA
ARTIFICIAL INTELLIGENCE AND STATISITICS V |
By: |
Doug Fisher (Editor), Hans J. Lenz (Editor) |
Format: |
Paperback |
![](/Images/divider_itemdetail_1a.gif)
List price:
|
£99.99 |
We currently do not stock this item, please contact the publisher directly for
further information.
|
|
|
|
|
ISBN 10: |
0387947361 |
ISBN 13: |
9780387947365 |
Publisher: |
SPRINGER-VERLAG NEW YORK INC. |
Pub. date: |
2 May, 1996 |
Edition: |
Softcover reprint of the original 1st ed. 1996 |
Series: |
Lecture Notes in Statistics v. 112 |
Pages: |
468 |
Description: |
This volume contains a revised collection of papers presented at the Fifth International Workshop on Artificial Intelligence and Statistics in 1995. The topics covered includenatural language applications, causality and graphical models, classification, learning, and knowledge discovery. |
Synopsis: |
Ten years ago Bill Gale of AT&T Bell Laboratories was primary organizer of the first Workshop on Artificial Intelligence and Statistics. In the early days of the Workshop series it seemed clear that researchers in AI and statistics had common interests, though with different emphases, goals, and vocabularies. In learning and model selection, for example, a historical goal of AI to build autonomous agents probably contributed to a focus on parameter-free learning systems, which relied little on an external analyst's assumptions about the data. This seemed at odds with statistical strategy, which stemmed from a view that model selection methods were tools to augment, not replace, the abilities of a human analyst. Thus, statisticians have traditionally spent considerably more time exploiting prior information of the environment to model data and exploratory data analysis methods tailored to their assumptions. In statistics, special emphasis is placed on model checking, making extensive use of residual analysis, because all models are 'wrong', but some are better than others.It is increasingly recognized that AI researchers and/or AI programs can exploit the same kind of statistical strategies to good effect. Often AI researchers and statisticians emphasized different aspects of what in retrospect we might now regard as the same overriding tasks. |
Illustrations: |
14 black & white illustrations, biography |
Publication: |
US |
Imprint: |
Springer-Verlag New York Inc. |
Returns: |
Returnable |
|
|
|
![](/images/spacer.gif) |
![](images/menu_shadow2.gif)
![](/Images/menu_shadow2left.gif)
|
![](/Images/menu_shadow2left.gif)
|
![](/Images/menu_shadow2left.gif)
|
![](/Images/menu_shadow2left.gif)
|
![](/Images/menu_shadow2left.gif)
|
Little Worried Caterpillar (PB)
Little Green knows she''s about to make a big change - transformingfrom a caterpillar into a beautiful butterfly. Everyone is VERYexcited! But Little Green is VERY worried. What if being a butterflyisn''t as brilliant as everyone says?Join Little Green as she finds her own path ... with just a littlehelp from her friends.
![](/Images/menu_shadow2left.gif)
|
![](/Images/menu_shadow2left.gif)
|
All the Things We Carry PB
What can you carry?A pebble? A teddy? A bright red balloon? A painting you''ve made?A hope or a dream?This gorgeous, reassuring picture book celebrates all the preciousthings we can carry, from toys and treasures to love and hope. With comforting rhymes and fabulous illustrations, this is a warmhug of a picture book.
![](/Images/menu_shadow2left.gif)
|
![](/Images/menu_shadow2left.gif)
|
|
![](/Images/spacer.gif) |