                           ˿µϣ20091028գ

09.10.28, ФϷ仡֣ܺԴҽԺ״
09.10.28, ѧšԻФ
09.10.28, ˼󺣹격ʿɱԭ
09.10.28, ҲǺ顶ҪѸ󻯣Ҳ󺣹鲩ʿ¥
09.10.28, ⡶Ϳʿۡ
09.10.28, ԽԽԡԺʿרHermiteչ庯ԡһĵĴ𸴡
09.10.28, һ»ʦѧ09ġԷʦ
09.10.28, ƽʼʱҽѧģʽͳҹҽ״ټȥˣУ
09.10.28, lw56102ԱɽӰ
09.10.28, ͿСƽ
09.10.28, һˡŪ磺Ͼѧѧ־ڵһƪġ
09.10.28, trapa־ĵһЩ
09.10.28, İϾѧ־ԵĶԡ
09.10.28, ðݵǱˮߡϾѧ־Ҷ¡
09.10.28, ·ҡ־¼ʱճ
09.10.28, ־һ㳡֤
09.10.28, ªƷиӽˮ֮ۡ
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09.10.28, Сݡй١ܼ
09.10.28, Ĵѧһʦ￿νΰ󡷡

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ФϷ仡֣ܺԴҽԺ

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ФʲôʵԳģҪ⣬ǹѧ硢й
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󺣹격ʿɱԭ

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(XYS20091028)

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ԡԺʿרHermiteչ庯ԡһĵĴ

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ѡѧѹû롣

(XYS20091028)

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Ρ1948ǰвѧҪоȾķءչ
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ذԼĽоಡΣ۱Ѿ
Ԥҽѧͽʵй㷺Ӧá23
3 Ŷϣ̺ͷΰϵо:1915ʾ̲
°,ŶϣͨһϵевѧоͶо֤
̺ͷΰ֮ڲѧϵŶϣ1948굽1952֮䣬֯
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26
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ƤΣա27

οף
1.ʿЦ.йҵйѧ磬
19861-4.
2.ʱ򡰲ҽʱձ200947?05.
3.磬Ҫµҽѧģʽҽѧģʽս.ҽѧѧ1980.3.
4. Engel G L. The need for a new medical model: a challenge for 
biomedicine.[J]. Science.1977, 196(4286): 129-136.
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200795178344.
6. Emson HE, Health, disease and illness: matters for definition, 
CMAJ, VOL. 136, APRIL 15, 1987,p811-813.
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health[m]. 6ed. : ѧ, 2007,10.
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http://ishare.iask.sina.com.cn/f/5150687.html
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WHOǵ쵼žͿ˼վϴӽ7µ
еͼҪи£ɫûϢĵ
µĵͼϣĻɫǷޡĴǻйǰ
ھ·ݱWidespread㷺ֻйͷޣʼ
ǻɫNo information availableϢַԼɡ
ûͨWHOWHOûյWHOţֻԼ
֪

http://gamapserver.who.int/h1n1/geographic-spread/h1n1_geographic-spread.html

(XYS20091028)

˿(www.xys.org)(xys4.dxiong.com)(www.xysforum.org)(xys2.dropin.org)

Сƽ

ߣͿ

һԼȥ£ƪڶСƽģȻ𣬶
ûˣҲֳʲô»ɣ 

ȸһѣȻ˵һﱻץˡѧͷѧ߶С
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硣ŹѶץϵϳʵǣСƽ
һ˷ǧУ£

⣺̯ һӹ˺̾ 
ʱ䣺2009-09-28 
ǧ928Ѷ 
ݣ£

ΪһºͲ˷ڽǣҵԱСƽͲͬȰ裬Բ
Ҳ˷ͷʹͷˡ13롣գ߶СƽҲʳ
ӹ˺ﱻ־¾ 
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ǻǰԺ뿪ԭɽסźӼǺ
лصũ1992һֱΪÿ賿𴲣ȥ
гˣȻ󱼲طˡ 
ź·Σ¡9175࣬ɽ
Ӫһ㼷˰̯˵̷źҲƽһڵߺ
ˡг·ȽխСС 
ʱһɫлγӱϾֻ죬ź㿴Լ
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Щˣ˳ʰһЩźƬ򳵴ӹȥһԲ
˵ 
ʱͣ˾Ӽʻ߳ź˵ֵܣǶԲ𡣡
źñҲʲô£һֶ˾˵û¶ˣ߰ɡ 
ʱڸʻλһ³ϵļҲ
˳ӳź˵Ҵ㣬𣿡 źûأ
ڵϣ˾䡰ƾʲôѽ˾һȰӣˣˡ
ӷ˾һ䡰ֱźӻ·ߴŵ
һԲźͷź㵱ͷڵϣͷѪֱ
ǰξˣһѱסɷͷ󺰣ƾʲôˣ 
̷ܱѸΧסӣóֻ˾ɽɳ񾯴
ɳ󣬶Сƽ˴˵ʵ

дСƽ˵Ļˮƽ͡ûндˣаµ
ζݶ֮Ϣ顣㶡Сƽ˷
Ƿ̰𣿲Ƿ𣿲ǹôеһ
ײƽֵĵز

ƭҲã񼲻ҲãСƽҪǸǻܹģϾ
ӴˣʲôСǣӾ紫ϢȻаѧΪ
һɷԹһٸѧ࣬ΪСƽԹ
ĴѧϰˣԵеࡣ˵ʲôأ˵ë˵
Ҳ˴˾ءߣ˻߷Ҳעҵ
ģڰ׷ڣ̫̫̫ûȤ

(XYS20091028)

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Ū磺Ͼѧѧ־ڵһƪ

: һ

־ѧIJCAI'09һƪģ

Yin Zhang, Zhi-Hua Zhou, Non-Metric Label Propagation, IJCAI'09

ҳΪIJCAI09ƪĵҪϵϮƪ

Julian Laub, Klaus-Robert Muller, Feature Discovery in Non-metric 
Pairwise Data, JMLR 5(2004), 801-818,

ҳΪLaub04

IJCAI091358ҳеڶε7У̸ǵĹʱ˵


Our main contribution is to propose the NMLP method, based on 
our proof that any non-metric distance matrix can be decomposed into 
two metric distance matrices.

Ҫ˵Ǹ֤ǴģΪ֮ڸԡ
زҲӦǵǷ켫ˡ󲻿ܣҪߴҵǣ
ȷ¡

IJCAI09TheoremTheorem 1  Theorem 2. Theorem 
2֤ǸTheorem 1֪ġ֤2ʱҪõ1
Ҫ˵ǣǹѶ1д֤Ķ2ڴ˵ҷǰ
һж֤˵

IJCAI09Laub04ܷǿҪ÷֤ˣLaub04Ҳһ
1ҳΪLaub1

Laub1 D is squared-Euclidean iff C is positive semi-definite.

ҳIJCAI09Ǹ1ΪIJCAI091IJCAI09Ǳ
IJCAI091Laub1ṩLaub04Ϊ֮һ

IJCAI091 D is metric iff K is positive semi-definite.

ҿˣӦһĶڳССͬؼһ
squared-EuclideanһmetricCKIJCAI091Ǵġ
ǰsquared-Euclideanдmetricҿ򵥵İж
дIJCAI09ҪľǻLaub04Ĺٿ2. Related 
WorkͷһΣTheorem 1ϷʲôдҪߴ
ҵIJCAI091ǹⱻдġΪLaub041֤ǵĶ2
дܡҳǵĶ2ΪIJCAI092

IJCAI092  Any squared non-metric distance matrix A can be 
decomposed into two squared metric distance matrix A^+ and A^- where 
A=A^ - A^-.

ҿǵ֤ǲLaub041֤ (ΪA^+ 
 A^-Euclidean)ǣIJCAI091֤IJCAI09
1Ǵġ

ȷĶ2Ӧģ

2 Any non squared-Euclidean distance matrix A can be 
decomposed into two squared Euclidean distance matrix A^+ and A^- 
where A=A^+ - A^-.

ȻLaub0412ע⣬IJCAI092Ͷ2
ֻEuclidean 滻metric.

ôΪʲôƫmetricôEuclideanʹԺǴ
ҲҪأ ûԵ޹޹ʵİҲûԵ޹ʵĺޡ 
ԭڶ2ѾLaub04, Laub04807ҳڶ:

The pseudo-Euclidean space effectively amounts to two Euclidean 
spaces one of which has a positive semi-definite inner product and the 
other a negative semi-definite inner productAn interesting 
interpretation of the distances in a squared-Euclidean space is that 
they can be looked at as a difference of squared-Euclidean distances 
from the 'positive' and the 'negative' space, by the decomposition 
..."

 pseudo-Euclidean spaceӦ2ġnon 
squared-Euclidean distance matrixsquared-Euclidean distances 
from the 'positive' and the 'negative' space,Ӧ2ġtwo 
squared Euclidean distancesΪthey can be looked at as a 
difference of squared-Euclidean distances from the 'positive' and the 
'negative' space'they' Ӧָ "non squared-Euclidean distance 
matrix.

ҪˣҪҷ
----------- Napoleon

(XYS20091028)

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־ĵһЩ

ߣtrapa

Ͼѧܽĵۣƺ˵Ķеһ
һͷˮҲһоݺģ˶ѧ
ʽܶ࣬ڵоҲ˹ܺͻѧϰ˼ܿ
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ռһЩо һݣ ҪҸõfunction
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ͳ عĿǼԤСȡδ·
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ͳķʹ磬ֻѡһáˣƪ
Ҫݺ͹ס

P&CϷ֤һ£Ѷƽɣõ
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Ϊӵۣsection 6, ˲е磬ֻ
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õ⣬ƵһʽԾǷҪµ·ӵ
档ԭġOne simple extension of the ensemble methods presents in 
this paper is to consider estimators of all of the possible 
populations which are subsets of the original populations.  If for 
example two networks in the population are very similar, . will lead 
to very poor results. Thus it is important to remove all duplicate 
(or nearly duplicate) networks from the population.  Removing 
duplication can be easily done by examine the correlation matrix. 
ܽһ¾ǣƪ֤˰Ѷ缯ȵãϰ
ãʵУʹԴǰ֤
еóƪۺǿڶߺˣ1972ŵѧߡ
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ܽڵġƵĹ㡣һǴҶ˵缯
ãϷ˼һЩܱȼкáѡҪɵ
·ʵͽһSection 2һѹʾʲô
many could be better than allϸƵķ̣17
ɴ˵óĽ۾ˡ˵Щ·17
󣩡һ˵Ӿǵ綼һظ
ʱԭġIt is obvious that there are cases where Eq. (17) is 
satisfied. For an extreme example, when all the component neural 
networks are the duplication of the same neural network, Eq. (17) 
indicates that the size of the ensemble can be reduced without 
sacrificing the generalization ability԰ĵȥⲻ
P&CһP&C˵All is betterʵP&Cۺgeneral
ʱܱǰֱۡʵϣҲȷ
ָһ㡣ٿ׶ Ŵ㷨ѡҪɵ磬P&Cָ
ϢԴؾеõͬؾʵ硣
ҵĿûףʵһСסʵͽ֣
ܶǸĵװɡ

ˣ˵˵ҵĿ

1.	ĵҪ۹ȫP&CУ ܵȻǶP&Cģ
P&Cʵһϵͳҽϵͳۺ֤дƪ۱
ҲûʲôԡûЩ۵ĹףƪȻܴﵽIJCAI
ı׼˵Best Student Paperо뷢չ־ǿԵġ
ص㣬ĹؼµдideaǴP&CģҲ
P&CĹȴԹ̸ʵ˷ѽ⡣ʵϣΪܵ
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˺ܶоP&C֤Ѷ缯ܣָ
ʱܻۣƪϵͳо
֤ʵ⡭ Ȼôд ǻܱIJCAI¼áIJCAI˹
õĻ飨õĽȷڻϣ־archive
ôдIJCAI¼ã˵Ҳˡ
ⲻܵƪĹж󣬶ƪµдȻΥѧ


2.	ΪʲôIJCAIܵġP&Cߣ˶˹
ܺͼȦӵˣۣԺҲû

3.	Bottom line ܵĿ϶ǻP&Cĵģûףϵͳ
ʵֺʵǹףúдһdecentлᡣǲ̸P&C
ҲԼĹǲɽܵġP&CҲȦӵˣ鷳
ˡ

4.	ܵĴ𸴺ʧ󵼶ߡ

5.	һӢͶĻһƪչ־ǳ
֡

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Ҵͨ׶ıȷɣһȷУô˼ǰͶ
ѡһ֣ƱծȯдиP&Cľѧ֤
˷ɢͶֱֻͶһֳڵߣͬʱʵʱͶ
ֱָֻã˵еʱͲͶƱˣǴ
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е֣֤ʵͶмֿܱеָãеʱ
ͶƱˣϷȻʵ˸ϵͳͨʵ֤ʵǵĽ
ΣԼ۰ɡ

Perrone&Cooper 

http://www.physics.brown.edu/physics/researchpages/ibns/Cooper%20Pubs/125_WhenNetsDisagree_93.pdf ¼P&C

ܵ
http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/aij02.pdf

P&CڶLeon N Cooper1972ŵѧǺǡ 
http://www.physics.brown.edu/physics/researchpages/ibns/cooperpubsframeset.htm

(XYS20091028)

˿(www.xys.org)(xys4.dxiong.com)(www.xysforum.org)(xys2.dropin.org)

Ͼѧ־ԵĶ

ߣİ

־Ӻܶ࣬УдƪPaperȺ
鼤߰Ҳкܶ๤Ҫûôæ˵д仹ʱ
䡣

David NgлĹ

רңȷѧĶشǲ
صģĺܶණܶʧȥ

ڡִš㲻̫вţ̫ʱˣǸл㡣

ڡKolmogorovͬĴ󲿷ֹ۵㡣˵İˮƽͣ
˵Ӣİʸ˵мֵѵΪӢİȫȡMBTA
õôߵڷܰɣΪܻһǲԵģ
ΪĻΪCSѧȦЦ

ڡº衱ҪMBTAP&CĵĹףԼֻڴ˻ϻ
Ŵ㷨ʵԣժҪȡ֤MBTA䣬Ӣ
Ĺʽ2014ȷ˵P&Cģ¿з
ˮ׼ͬ⣿ԼP&CǾײİø
ģ϶ͨ)ڱҷĵһظȷָ17-20Ƶ
֤ĵMBTAԼȡ֮⻹ԣ⣬Ϊѧ
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ڡTongTonģѧԺԺʿLeo BreimanѹˣLeo Breiman
˵ܲǳϮѧӦʵȨ⣬˵˵ġһ򲽡
ѧͬӦýСٴ֤֡ûпû
P&Cĵĵ6𣿹ʡP&CõѾ˵һСڴ
װͿӦ׵ǣЩȷ֤ݣDavid Ngܽᣩ
СͬѾˣڴͬл֪ѧУҲͷ
Ź⻷ܡΪǲỨʱȥо⼸ƪģ

ڡ߸˹֪⣬ִš˵ڽˮҲ
ش

һû˵һ׵ѧΪ˿ԩɨ
صģȻˣܷۡ˼ȵੲΪʲô֮
MBTAܣҾǾϲġMBTA
Եģ֤Ѿ㹻ֻ½ۣЩνСͬ
ʲô

(XYS20091028)

˿(www.xys.org)(xys4.dxiong.com)(www.xysforum.org)(xys2.dropin.org)

Ͼѧ־Ҷ

ߣðݵǱˮ

һ˿ϳġǱˮߣӲðݡдһʹ
飬Ҫȥ֤¼ʵԣ붼Щˡ˿
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Ѹѡ

ǰҾͶ־ĴҲ¡ֱ꣬Ҳ
Ϥѧǿѧ轫"Knowledge and Information 
Systems"ϵһƪ¡ͳƵĻѧϰļΪͳѧϰ˹
ܵһţ20ɫӦЧʶ𡢻ȣ
˹ɵо䡣Ȼأͳѧϰȫ
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ܵ¹ڼѧϰҶֻ֪һ½ۡ"
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ڣ̫ûȻVapnik֧JordanͼģʽLafferty
ȣ־ĳɹۣΪͳѧϰһţ
Ҳû̫顣οᣬ۲ʿֶ࣬ʱգһͳ
ѧϰһġ"shy"˵־ܽݺʹϣ
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ڰලYarowskyѵ1ѧϰBlumMitchellЭͬ
ѵ2ѧϰǾ䣬һtri-training3ѧϰ
Ĳвδθtri-trainingЭͬѵΪһɵĴ
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ΪѧϿƪtri-trainingӦ£ҵϷ
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С۵㣨һͶ˰ЩܶУұ
Ҫ꣬ҲƵ۵㣬˿Ŀ£ԽԽСѧµ
ƫ⣬ˣôֻ࣬һƪ£
ô֪־ѧˮƽ

(XYS20091028)

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־¼ʱճ

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Ҷ־¼ļ㿴

1о˿ַǷһ־¼20ӣ˸
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2ĹؼMBTA״η֣P&CĵĹס

3ܼȻһʱд˵һظڿһ֤ݺѡĬǲ
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4ѧҪƾʵݣDavid NgĹ

5֧ܵߵķûҪǰĵ˵£
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6ܲվ˵Ĭϣ˿ıûٽеı
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(XYS20091028)

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־һ㳡֤

:
ַܵӳϮ. 뿴.

-----------------------------------------
־ѧϰھ

һƪܺõ
־. ѧϰھ. йѧͨѶ, 2007, 3(12): 35-44. 


-------------------------------------

л¥ϴ

д÷ǳ⣬ʽ

е֣Ƿ롣ͼƬȫԭʾKernelNeural 
NetworkͼƬδ˵ֱӴӱ˵CopyPasteģ磺
"Learning with kernels" by Bernhard Schoelkopf et al. 21ҳ29ҳ
ֻһڲѧ棬˴ţǷڡйѧͨѶ
Ļ治֪ôԵİȨӶ

Ҳ˵˵

You can search "Learning with kernels" at http://books.google.com/ .

------------------------------------------------
ԭļ:

http://www.ieee.org.cn/dispbbs.asp?boardID=62&ID=56641

(XYS20091028)

˿(www.xys.org)(xys4.dxiong.com)(www.xysforum.org)(xys2.dropin.org)

иӽˮ֮

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Ԭ¡ƽǷӽˮ֮˿ǻ𱬡Ϊһˮ
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ʳrice science for a better world,ܼ򵥵ӢIRRI
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ģӽˮĳɹܷϵʱľٹơʱ
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ͣõԶԶСģھЧЧ档Ԭ
¡ƽӽˮ֮

ڹˮ

(XYS20091028)

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Լ͵ġƪйءӽˮµ߼⡷й׾ĵĲֵĻظ

ߣHus

ԼͶ׾ġˮо(IRRI)ӽˮвɣ˵
ôҲΪΡ[1]رᵽVirmaniӽӽˮ
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෴ҲѾע⵽ˣйһЩԡҵӢ/
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Ӣдġ˼ϡǿѧֻ˸ֵۣ
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Virmaniӽӽˮ֡ģ˵
⡣IRRIرΪһachievement˵
йӽˮоеĹʵλˡͬģһ켸ִо
о߶ܷģԼܱܵйΪ٣ǲǣ
쳯ǿѧˡ

(XYS20091028)

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й١ܼ

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Ϊ֪ейˣڷӵվϷ˲֪Ƽ
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ӿԴݽߣһ㣬GDPж壿ϵ潭й
ĳ֧ӵ׳֣ӴɿݣûˮȣҲ
ܺȡɵİǷȻʱˣպɽӰɣ
ʲôʲôֻ̡Ṇ̀ҪЩٴգЩǻʶһ
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й⣬ѻɽ̸ڣǧ˿ƣѷβ󲻵
йĿǰ౯(֪ʶӺʿɱ)ԴڴˡڿƼ棬
Ҫָй20ŵߣйĸܡٺͼٴ
ûеõЧƣûȡøǻġڹ˿
ѧҽѧǹƽģûƫӣƽɫʺ˼
ʶеŨϸŵƽҲӳıԺ͹ͬļ
ֵȡˣйϻŵƽҲǺܺõ顣Ȼй
20ŵҪָǣѧҽѧǰ
ָ˱ǰᣬһȽйĽɳģ
ǻ񽱹йɵġ

һ棬йӣܴӣ
İʵʵˣʵ£ӪɵɵĻ
գǵϣҲһйΰʱһõĿʼ


2009.10.26

(XYS20091028)

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ߣĴѧһʦ



ܱǸҪռ˿ı档

Һ°ʵ͵°ĵ¼ص
飬ּءǿǵʱĴѧѧ᳤ǵ֮
һǰĻõ˵Ͽɣں棬ȤѲο
ĵҲǵ֮һĻҲõϿɡ

˳˵䡣ŵĴѧѧԺһף
У԰һƬƽһҲûС2007꣬Уѡȫ˴
ѧǵǹ˸׵׺ֵĴǵǡףͬ־ѡȫ


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һʮְдΪһǷǵ
Լǵվĵطеϣܶǵʦܡ 
ʦáѧҲܹͬԭ򣬰ͬһд

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