語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Diagnostics and extrapolation in mac...
~
Hooker, Giles.
Diagnostics and extrapolation in machine learning.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Diagnostics and extrapolation in machine learning.
作者:
Hooker, Giles.
面頁冊數:
127 p.
附註:
Adviser: Jerome Friedman.
附註:
Source: Dissertation Abstracts International, Volume: 65-09, Section: B, page: 4648.
Contained By:
Dissertation Abstracts International65-09B.
標題:
Statistics.
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3145521
ISBN:
0496044648
Diagnostics and extrapolation in machine learning.
Hooker, Giles.
Diagnostics and extrapolation in machine learning.
- 127 p.
Adviser: Jerome Friedman.
Thesis (Ph.D.)--Stanford University, 2004.
All the ideas in this work are designed to be fully general and compatible with any machine learning algorithm.
ISBN: 0496044648Subjects--Topical Terms:
182057
Statistics.
Diagnostics and extrapolation in machine learning.
LDR
:02278nmm _2200313 _450
001
162875
005
20051017073532.5
008
090528s2004 eng d
020
$a
0496044648
035
$a
00149376
040
$a
UnM
$c
UnM
100
0
$a
Hooker, Giles.
$3
228020
245
1 0
$a
Diagnostics and extrapolation in machine learning.
300
$a
127 p.
500
$a
Adviser: Jerome Friedman.
500
$a
Source: Dissertation Abstracts International, Volume: 65-09, Section: B, page: 4648.
502
$a
Thesis (Ph.D.)--Stanford University, 2004.
520
#
$a
All the ideas in this work are designed to be fully general and compatible with any machine learning algorithm.
520
#
$a
Finally, I advocate a modification to the Functional ANOVA that uses this estimate to avoid the effects of bad extrapolation while retaining many of the useful properties of the decomposition.
520
#
$a
I present a suite of tools for understanding high dimensional prediction functions that are based on the Functional ANOVA decomposition and argue that these are optimal in an idealized setting. I then show that they can be distorted to an arbitrary extent if the predictor space contains large regions of extrapolation.
520
#
$a
The subject of this thesis is the interaction between the problems of diagnostics and extrapolation in Machine Learning.
520
#
$a
This thesis gives a criterion of extrapolation and details tree-based methods to evaluate it. This methodology provides a comprehensible representation of the distribution of training data and a diagnostic for functional behavior in regions of low data density. I then discuss the issue of making predictions at points of extrapolation. I suggest a strategy for stabilizing a general learning algorithm away from training data that is motivated by a Bayesian heuristic not unlike ridge regression and which bears some resemblance to Kriging.
590
$a
School code: 0212.
650
# 0
$a
Statistics.
$3
182057
650
# 0
$a
Computer Science.
$3
212513
690
$a
0463
690
$a
0984
710
0 #
$a
Stanford University.
$3
212607
773
0 #
$g
65-09B.
$t
Dissertation Abstracts International
790
$a
0212
790
1 0
$a
Friedman, Jerome,
$e
advisor
791
$a
Ph.D.
792
$a
2004
856
4 0
$u
http://libsw.nuk.edu.tw:81/login?url=http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3145521
$z
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3145521
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000001368
電子館藏
1圖書
學位論文
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
http://libsw.nuk.edu.tw:81/login?url=http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3145521
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入