                           ˿µϣ20091025գ

09.10.24ûи
09.10.25, ӡ֯ҩ׼嵥κҩ
09.10.25, ľԭʿϽͨѧУ
09.10.25, ·˼סͤ¼ľ޴ĳš
09.10.25, ȷ硶Storm Rainfallڸͤĵ8ƪ
09.10.25, ʷgdnh㶫ѧٹ̡Ժʿ̡ļ
09.10.25, ִšܳꡢѧ־֮Ҽ
09.10.25, ־ԡһ֪ܵˡ͡İ𡱵˵ظ
09.10.25, Ƶ塶ԡĹϾѧѧƸ־Ե֤ݡʡ
09.10.25, Hui HuangϾѧѧƸ־Ե֤ݡ͹
09.10.25, yeslala˵˵ҵ󺣹꾭
09.10.25, ũ񡶡ӽˮ֮Ԭ¡ƽ֮
09.10.25, Husˮо(IRRI)ӽˮ
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09.10.25, SaijunɱԣʲôԿ˷
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09.10.25, MrMathematicaĿϵ¼Ϊʲôóԣ
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09.10.25, ʯϵ»ʽԪء
09.10.25, ϴѧһλ鲩ʿ潭ɱ һ

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

               ֯ҩ׼嵥κҩ

                             ӡ

й°һҩĿ¼֮һΪгҩҩƬҲ
ȥ

֯°棨2007棩ҩ׼嵥κгҩ
ҩֲҩкҶƬҽҲ÷кҶΪҩ
ʵԭӡȵͳкҩԽСҩݶԷкҶ
޼أǽŴйġҲ鵽ҩйطкҶ
1935ġƬ²Ρ

֯ҩ׼嵥İ棬嵥ص㼲
ЧȫͷϳɱЧҩڻ߲ο

http://www.who.int/medicines/publications/essentialmedicines/ChineseEML15.pdf

(XYS20091025)

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

ԭʿϽͨѧУ

ߣľ

2009715ϽͨѧʽͨѧУѧίԱ϶Ĳʿ
ѧλĵ³ϮʵʽΪءѧУѧλίԱȡ
ѧʿѧλоʦʸϽУ´ձʾ
ĳǷᱻУְϽǽֱѧУ
ĳ򡱾ɽʹȨˡ

3ˣϽͨѧٷվڡ쵼ԭʿ
ϽͨѧУڶвǣУ쵼иӣ
츱УûС

ǷʵΣҪϽ󡣲ͨʦѧ
ǻ᲻ϵٷشǡ޿ɷ桱쵼ڡȷʵ
ٳˮ

ڴˣѧٳ֮ԭڹٳá
߱ǹԱ߹ٷĳҪߡ

ѧٵˡ

¼һϽȨƷˣ
ͺñСײ˵Ĺ¡ϡûʵط̰ʵʺ
˴֪ȴбҪʱҲһ¡Сײ˵Ĺ
Ӧ֧˭˭ͬ˭ףֻ˵ʱı硣

(XYS20091025)

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

ͤ¼ľ޴ĳ

ߣ·˼

ҹڸͤϵĽһ.һ,ֱ
޴ĳ.

ǰķ͵, һɸΪ,J. Egger, 
MajewskiE. Jadin  Pakistan Journal of Meteorology. һ
ߵĲǲ? 

һ,lurenjiaJ. Egger ȥһ, Ҫ
ݶƪ.˭֪ڻ˵,֪ƪ.ͬʱҲظ
ǵ, Ҫȡһʩ. ҵżĻظĸ.

һƪDetlev Majewski  Evgeny Jadin£Ҳȥʵ 
ҸоûҪˡѺԣΪı,αһƪ
׷Լ,θԼΪԼЩ.

,ǻϿؿߵַ,Ժʿǰ,α·
ԼԿƵ־,־ʵڲΪϤ. ԭ߸֪. 
Ҳ.ڲصЩα׷Լ, ۸
µĳʱ. ô޷찢.

ߴһȫµʽ. ˷׷Լ. һµ"׷
".

ҸоߵѾ,ҲԶ,ºηչ,Ͳ
Щ˵˵.

   Dear Mr. Lu Renjia:

   Unfortunately, my name got onto this paper without my consent.
   I do not have a pdf of this paper, of course. Sorry, but I cannot help
   you in this case.

    Best regards

    Joseph Egger

On Thu, 22 Oct 2009, lu renjia wrote:

> Dear Prof. Egger,
> I am a graduate student in China meteorolical admistration. I am now working
> on China synoptic scale westher system. I am really interested in one of
> your papers published on Pakistan journal of Meteorology.
> "Progress of Research on Potential Vorticity and its Inversion" by J. Egger*
> and Q. Z. Chaudhry in January of 2009 (vol.5 issue 10 of  Pakistan Journal
> of Meteorology). From the title, I think it is a very interesting paper and
> will be helpful for my current study. But I cannot get this paper. Do you
> mind send me a pdf of the paper. Many thank!
>
> LU renjia
> Ph. D candidate
> China Meteorological Administration
>

(XYS20091025)

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

Storm Rainfallڸͤĵ8ƪ

ߣȷ

ҲùE\Pͨ۽жٵоΪù
E\Pͨ(E vector)о߿նٵE.K.Trennerth 1986
ġE.K.Trenberthǿţˡû뵽й绹иţ
ģͤ˸߿ռǿMorning StarһҲһԼ
ªţ

У˸ߵĵڰƪĵҪ׼о
ͤǸţˣͬJ.R.HoltonT.Matsuno̩ɽ
ﲢˡҶԼĹªŸе𾪡ϽҸߵĵڰƪ
ĿϧһʱҲйѧBStorm Rainfall֪
ҵĵĵӰ

⣬˵ģ2008JOHN P. BOYDԵڶJOURNAL OF THE 
ATMOSPHERIC SCIENCES־ΪRossby Wave Ray Tracing in a 
Barotropic Divergent AtmosphereģжԸߵǺϣ
нἰѧҡJ. BoydԵһֲֻᣬʵֵ
ζһҲǸеԻƪµĵһ߽Lu, Chunguֿ
ӦλˡΪʲôأҿΪǻˣ֪ߵĵڰ
ƪɣ

--------------------------------------------
ߵҪ׼

ҵĲʿġǣE\Pͨ۲׼ȷϲ
áĹE\Pͨ׼ȷϲãɿѧؽ͸߿ռ
ǿ۶ԱԤҪӦüֵۣѧJ. 
BoydѧJAS,2008Ϲۣ׼
QBO£SSW͸߿ռ٣ѹHolton (1968), 
Matsunno(1971) Gao(1990)òĴ͡

(XYS20091025)

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

gdnh㶫ѧٹ̡Ժʿ̡ļ

ߣʷ

һΪgdnh20˿վһƪΪ㶫ѧ
ٹ̡Ժʿ̡£߶󣬸оȫ
ʵ۶Թ㶫ѧǶͱˣǼġ

ȣҿԺϵظ߶Ҳڹ㶫ѧһв
ɲȻûʲôʵʵȨѧУ飬һЩ쵼Ļ飬
ҶвμӵģֱڣҴûλУڻ˵Ժ
ʿ̸ϡ⼸ڼص㹤Χ
УԺʿչġˡôôأԭ10
9ѧУٿĽϣλܽĹʱˮѧ
ԺԺıĽĹ룬ҵһ˵ңˮѧ
ԺҲûָΪͣ˵ʹָľͣ
ĿгɾͺѧˮƽѾﵽԺʿˮѧԺдѵˮ
ѧԺ޴ĵزûʵװͿй
ѧԺԺʿѡΪԺʿ

ڹ㶫ѧ⼸ڼص㹤Χ͸У
ԺʿչġֱǺ˵˵ÿһ˶֪⼸
ѧУĹĽѧеȹ֮⣬2006굽2007ص㹤
ǽıƽѧˮƽ2008ص㹤ǡ격격׼
֮2009صǡЩÿһҪȫУ
ȫԸܰѡص㹤ȥΧơһԤ֪ġԺ
ʿ̡չأһ֪˾֮

ΣڡϵϺҵЧ㶫ʡѧһȽ
⣬˵͡һֱǸಡоϺһϲͨǡ̰֮Ϊ
С͸Գߵ֪ˡҲ˵ˮѧνġಡһ
ָȰ࣬ҲϺͱ࣬ԡϺΪо
£ҾйڿȫݿҵƪϺûʮζضԶϺֳЧ
ʵ顷йˮ19944ڣʵȾװ߸״(WSBV)İ
ڶϺѪҺѧоȴ20003ڣ廡Է
Ϻ߹ܵӰ졷տѧѧ20046ڣ廡
Ĥ׶ԷɱϺ߹ܵӰ졷ȴѧ20056ڡУ
ϺûʮζضԶϺֳЧʵ顷廡Ĥ׶ԷɱϺ
߹ܵӰ졷ǵһߡ˵Ͳ㡰ࡱоҲһ
ֱڸ㡰Ϻࡱоô˵Ϻࡰһϲͨأ

ڡϵϺҵЧĿı⣬㶫ѧشտ
տȫϺֳأ֪ġ㶫ѧǰտ
ˮѧԺϺֳоѧƣ㶫ѧоĶϺ
ֳԼƹ㣬ҹϷϺֳķչû伸ʮĶϺ
ֳоɹĻۣȫ걨Ŀң2006
ȵĻĿϷձϷϹʾġνѾͨ˹㶫
ʡƼɹҪ쵼աΪ㶫ʡѧһȽƣ
صָأgdnh֤û֤ݶ˵gdnh֪
Ҫܸ桱ķε 

͵Ϊ˺ͿгɾͣԼΪܿиУĹɼڹ㶫
ѧˮѧйۣڴҲ׸ԡָǣͨ
gdnhȫģǲεУ䱳Ŀʵ˻ɡ
ǽ֮вɸ˵Ŀ֮ʵǷҲӦþأ

(XYS20091025)

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

ܳꡢѧ־֮Ҽ

ߣִ

׸˫һִţİ
˳ȻԴۣҪֹۣɴҲֶΣ


ȣܣû⣿شǿ϶ģԼ⣺
Ӣͬ־һӢ־ʡԲ̫صã
ָ⣺ⳭϮԾһҲǸȨרңҲѧ
Ӷ꣬ģܳϵǰⲻӰǰ̣
һҲϣϾ͵game overĶش󣩡Ϊһ
ͬѧˣ˭أΪһ863ר鲻
ͬ⣬ʤ

ľ

ĵİ20009³ģյʱоչڣ
Ӧ20003Ͷ壨ʱĻ3º¼ãŶ3³Ҳ
ˣ㣬20003GASENͶоչ20016²
ͶIJCAIʱϲ15¡Ϊʲôоʱ⣬ԭģ
ɹҪǺܸߵģôʶӦþڸˮƽʻϷ
ͶIJCAI01İ汾ɫ2Աݼܵˮƽ
ڸ㶨ӣΪʲôôأѵƫҪеIJCAI
۲ϳڼAAAIICMLNIPS ͶЩ鲢IJCAI
ܵʱһᶼɽһΣƫƫ͵15
£ڼܷ͵ƪԭ򣬻ķ
ƽѾٱбˣܣͷٱˣıȷֱ0:1

˵ϮǷ⣬ǰ⵼IJCAI01ʡ˲
̫صPerroneףһǰİֻ12ƪοףIJCAI01ȴ
20ƪʶվסš2000ΪPerrone׵
Դ1/12һȴΪС 1/20ݻͬ£
Perrone׵ԲӦñ仯Ȼġԭ򡱲ô
ϵġʡԡӦöΪ ʡáΪʲôʡƪ
أԺͷıȷֱ0:2

⣬ɲ򵥣ҪеۺϷҲԽм
ѧϰǺǣͨİĵһߣʱ˶һİ
ڶߣʱ˺˵Эָ⣩GASENΪһߣ
ΪڶоչԭʲôڲϺᵽʿ
ܾ˼ĻأԼԩҾø̫棬ܵ
ʦʲô߹ٻ˵ùȥ˶һûбҵ⣨Ҫҵ
һƪ̫ˣҲáоչݣֲ裬
Ϊʲô˵һأİֻ⣬ûзԭôҸ
棬ֵַᳫۺϷƶϵʱܺ⹲ͬ
GASENΪ̫ҪһоչˣҲ
ᵽΪʲôĸ15µԭ򣻵Ȼֳһ⣬
15ºΪηָҪأƵ20016¼ڳˣ
ѹѹȥһƪ̫Ҫ¼һ£Ƿ룩
ҪһʡPerroneףʹ̥ǣ˼ᵽ
ʡԵԭ򣩡Ĵ⵱Ȼ磬ܶԸĵĹ
ʵķԾ˳һδԾݣۺϷе㿿ף
ͷıȷֱ0:3

ҲʣʡһþôãȻô򵥣
IJCAI01ժҪоһףӢİժҪ۾ᵽ
Ķ...۷֤many is better than allMBTA
ȻһֻŴ㷨GASENİȴֻ˵һֻŴ
㷨ѡ缯ɹ췽渽ϵƪժҪҶ
֪ժҪ۵ҪԣٰPerroneʡԣͬ
һȽʵ2ݼ޹ؽҪڷãֵý
IJCAI01󹦸ɣ˵һ棬ʵ˴Ʒ
Ȼһ£˶һˮƽȻʾӵڶժҪ۵ĶԱʹ
ܵġ⡱ۼԣƣȷֱ0:4

ڱһֱƸĹ֤MBTAİժҪ۾
޴⣨ʱ̫֣ȻĵĹʽ1114ᵽ֤
MBTAĿŵ˵Լ֤ģֻ˵ʽ1114֤ģ
1114ĹǰᵽPerrone׵ó˹ʽ10ȥ11
14ϰɡѵʱûʶ׵ҪԣûдժҪ
Ȳ½ۣٿӢİ1720Ӧİ 1114Ӣİ
û16İ10Ĺʹ1420һǳɣPerroneǣң
ڵǰ޹˵ľ硣ôλ϶ҪʣӢİ1720İ1114
˭һ֪ܵˡרҵѧ任Perrone12ҳΨһ
ʽ任Ӣİ 20ףѧ˸ߴСҲܿ
֪ܡƵҪл֪ܡĹףһ˲ȥϸо
ȻӢİ1720ԼĶȴ Perrone׵ĵ12ҳ17
20֤MBTA϶йˡȻӢİĹʽ2014֤ʵ
ܣܵѸ󣬱ȷֱ0:5÷ֺܹؼٵ1ַͿ
ȡʤˣ

߶ǰŴԭˣIJCAI01
ѡԲPerroneΪPerrone12ҳĹʽƵ
IJCAI01ȡΪһΪǬŲ񹦽İ汾̥ǣ
ɵڶ˵һܵ˷˿ܻ˵ŵһʵ
ʩǬŲƣ˵ɵģΪº÷ԲŽЭܲ
һȷʵܰܵæڹ⣬ô£Ĭ
OKڽӵٱԼд˻ظ£ֻһߣȷ
Ӣİ湫ʽ1720ĹףЧˡ֪ܲΪʲôҪ
MBTAأܵķʷˣûMBTAڴ˻ϵGASEN
29ܵ࣬32ͳɳֻӲƴˣ֪ܡ
ϵܱر⣬Ҳ޷˶ѡܵķʹþʧأƼӾ磬ȷֱ
0:6

Ӧø渺ˣʱܵĳ˷˿Wei Huangshyȣ
ܻ飺ҿ֪ܡƵҲԳΪܵķ˿ͷ
⻷ҪΪѧϽ֪ܡ˭Ҳʶ
֪ܡİֲֺţӢİ湫ʽ1720ܡӵ
֪˿ѶȽϸߣƴΪŤתȷֱ 6:6߾֡

˭Ӯñʤأѧˡվ˳˵XX
ίзȨΪʤȷӦ6:7ܼ˿ͬ⣺
˵ǹڿίһ˵IJCAI01רҼIEEE Fellow֤
أǱֻüУʱԱͻȻһ־ϴ
ȥΣǣһ˶Ա˳ֹûʤֻû
ȥΡ

ҵĽҲȽȻδֻҪøλȹһ̣ҵ
ĿҲʹﵽˡ֪ܡĲƵ̫ۣ࣬
Ļظ͸˵ˣϴڻı׼Ͷ彨鷽˻
޸ĺٷǺǡ

[1]֪ܡƵμ
http://xys4.dxiong.com/xys/ebooks/others/science/dajia10/zhouzhihua6.txt
[2]оչİժҪ
һѡ缯ɹ췽, ѵ֮, ʹ
Ŵ㷨ѡ񲿷缯. ۷ʵ, 봫
ͳʹиķ, ÷ܹȡøõЧ.
[3]IJCAI01ӢİժҪ
"...In this paper, the relationship between the generalization 
ability of the neural network ensemble and the correlation of the 
individual neural networks is analyzed, which reveals that ensembling 
a selective subset of individual networks is superior to ensembling 
all the individual networks in some cases. Therefore an approach named 
GASEN is proposed, which trains several individual neural networks and 
then employs genetic algorithm to select an optimum subset of 
individual networks to constitute an ensemble. Experimental 
results..."

(XYS20091025)

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

ԡһ֪ܵˡ͡İ𡱵˵ظ

ߣ־

ã
 
˿վ20շˡһ֪ܵˡΪ Ͼѧѧ
Ƹ־ѧԡ£ڵ֪ºд˻ظ22
˿վҵĻظͬʱİ𡱵дġϾ
ѧ־һ⡷ҵĻظδ˿ʱ
Ҳ͵׫д˽һУҪллİ𡱣߾
ǡһ֪ܵˡ߱ĵط֣ˡ
ʱ̡Ĺע˺󣬡һ֪ܵˡд˽һָءѾ
ٻظΪԼĹҪڻظ޶ָ֮С
վϿһЩ֧ҵ£Щ֧ҵˣҲ֪Ƿʶǣ
Ҫ˵һлл

յδıIEEE Transactions on Knowledge and Data 
EngineeringࣨĿǰδ˿Associate EditorţȫŸ
棨Ϊ֤ʵԣṩ

> Dear Zhi-hua
> 
> I must say that I have been impressed with the effort
> that you have put into handling TKDE papers, and your
> professionalism in handling them.
> 
> I have received an email from a "free email" account
> pointing me to www.xys.org, which I have been following
> even before the email.  I will ignore such email and
> will rather trust what I have observed.  You have done
> well as an AE, and I believe you will continue to serve
> TKDE well.  I am therefore writing to say that my
> judgement will not be affected by what I have read at xys
> since no one is guilty till proven beyond doubt.
> 
> You have done very well, despite your young age.  Keep
> up the good work!
> 
> regards
> beng chin
>

Ѿŭˣ˿޳ܣ޳̶ܵȡҲ֪һ
֪ܵˡ͡İ𡱸йصĹڿࡢʻϯȥ
ź˷ܶΪùѧܹõͬеأ
ΪһЩĿģȻһе޶ĨڡǿԲ˼ҵ
Ӧù˼йѧߡйѧ档йѧ籾Ѿ
أһɣйѧоǲȥ

ǿԲԼļֵæڶڼˣȴ
˷ҵʱ䡣ڴ֣κָأҲظ
ѧ⣬ֱڿٱֱڵλѧίԱٱ
κйڿλԾٱʽ롢רҵĵ顣

лл





־
20091024ҹ

(XYS20091025)

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

ԡĹϾѧѧƸ־Ե֤ݡ

ߣƵ

˿϶й־ʦ֮ǰµһЩ飬ֿ
һ֪ܵˡٴηġĹϾѧѧƸ־
Ե֤ݡһ
(http://xys4.dxiong.com/xys/ebooks/others/science/dajia10/zhouzhihua6.txt)
ṩνԡ¡֤ݡؼġ֤2У
ʽpcĵһi<>new ǹʽ17һi<>kΪkǸҪ
E[m_new.m_i]C_{ik}ұߵһһȻ2Ҳ
һˡ" ҪģҲһ·֡˳ں棬Ҳһ
££

ǰϵPerrone and Cooperұߵһ\sum 
E[m_{new} m_i]ڶE[m_{new}^2]ʦұߵһ\sum C_{ik}, 
ڶE_kΪϵͳһʦеһPerrone and 
CooperеĵһͬC_{ik}=E[m_{new} m_i]δȣֻǸ
ݾٱȼһ£ĻʦеĵڶPerrone and Cooper
еĵڶôҲȲΪʲôȻ2Ҳһˡ

ġԡʦ·8֮űһ֪ܵˡָ֤
Ҿú֡ƪԡĿͨhttp://scholar.google.cn/֪
ѱ360ΣΪҺܺ棬֪οģ˴Щ
5ƪڹȨ֪ڿͬʱPerrone and Cooperʦ
µ[1-5](ͨgoogle)Щ߶ƪ
֮ıʲͬҪԣһٱ˸Ӧ/ǰɣ

[1] Domingo Ortiz-Boyer, Cesar Hervas-Martinez and Nicolas 
Garcia-Pedrajas, CIXL2: A Crossover Operator for Evolutionary 
AlgorithmsBased on Population FeaturesJournal of Artificial 
Intelligence Research 24 (2005) 1-48.
[2] Nicols Garca-Pedrajas, Csar Hervs-Martnez and 
Domingo Ortiz-Boyer, Cooperative Coevolution of Artificial Neural 
Network, Ensembles for Pattern Classification, IEEE TRANSACTIONS ON 
EVOLUTIONARY COMPUTATION, VOL. 9, NO. 3, JUNE 2005.
[3] Leandro Nunes de Castro, Fundamentals of natural computing: an 
overview, Physics of Life Reviews 4 (2007) 1C36.
[4] Nicolas Garcia-Pedrajasa, Colin Fyfe, Immune network based 
ensembles, Neurocomputing 70 (2007) 1155-1166.
[5] Imran Maqsood, Muhammad Riaz Khan and Ajith Abraham, An 
ensemble of neural networks for weather forecasting, Neural 
Computation & Applications (2004) 13: 112C122.

(XYS20091025)

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

ϾѧѧƸ־Ե֤ݡ͹

ߣHui Huang

ҷǳʧĿһ֪ܵ˵ġࡷһֽ۸Ϊ֤ݡ
ڡࡷһУȻ˹ʽƵ֤Perrone&Cooperܵ
־¡֪ܵ˻ȥͶ߰˹̹۳ϮΡ
һע⵽֪ܵһᵽ10ꡱ

ǳϮ˼·ҪϸڵҪڴԼ10ܵ
ˣ

֪ΪʲôҪ˵־ĹǳϮ10ǰġ£־
8ǰˡ

ΪǷϮҪͬп

ΪʲôҪͬпͬһչȥооȻ
ԽԽϸ£δܿоͬһĵ𡣴ʱͬп
ǷϮΪ׼ȷı׼ԣҶͬеĿ˵飬
ҵĵ

Դ½ѧһĲΣժƪġƪ
ͬʱPerrone&Cooperº־£ҲǼã
Ƭ½ЩУͬ
ƪµġ

һ
http://www.dcs.shef.ac.uk/intranet/teaching/projects/archive/ug2004/pd
f/u1sn.pdf
ӢһҵģߵλΪThe University of Sheffield

ƪУPerrone&Cooperµ[57]Ϊģ
Selection has been mentioned in many papers. A heuristic selection 
method was used by Perrone and Cooper, [57] where they train a 
population of nets and order them in terms of increasing mean squared 
error. Nets with the lowest mean squared error are combined in an 
ensemble.

־µ1[39]Ϊģرעǣѡһ
֡ף39,45,51ûPerrone&Cooper
Most ensemble approaches use all the nets available for combination, 
however if only some of the nets are combined it may be possible to 
achieve a higher generalisation. [39, 45, 51]

־µ2[39]Ϊģ
More recently, Zhou et al. [39] claim most approaches ensemble all 
the available networks rather than selecting some of the component 
nets and ensembling them which they found to be a lot better. [39] 
They take a genetic algorithm approach to selection to show that many 
nets when ensembled can be better than all the nets. [39] They present 
an approach called GASEN, (Genetic Algorithm based Selective Ensemble) 
which firstly trains a number of neural nets. Random weights are then 
assigned to the neural nets and the genetic algorithm used to evolve 
the weights so that the fitness of the neural nets in the constituting 
ensemble can be characterised. To create the ensemble selection is 
made according to the evolved weights

־µ3[39]Ϊģ
A possible method proposed by Zhou, Wu and Tang [39] could be 
beneficial to evolve effective ensembles by selection. They present 
that the combination of many could be better than all [39] when 
ensembling neural networks. They argue that most approaches use all 
the available neural nets to create ensembles, even though the 
integrity of such an approach has not been formally proven. [39] 
Therefore, a selection of nets could be compared with the combination 
of all the nets to see what method returns the most effective ensemble.

ԿñҵΪPerrone&Cooperº־
ǲͬģΪ־ĳϮ

׶Niall Rooney, David Patterson, Chris Nugent. Pruning 
extensions to stacking. Intelligent Data Analysis, 10(1):47-66, 2006
ߵλΪUniversity of Ulster at Jordanstown

жPerrone&Cooperµ(رֱBEMGEM)[23]
Ϊģ
The simplest ensemble method for regression is referred to as the 
Basic Ensemble Method (BEM) [23]. BEM sets the weights i to be equal 
to 1N . This method does not take into account the individual 
performances of the base models. Bagging [2] is equivalent in its 
integration approach to BEM, however it requires that the base models 
be generated using random sampling with replacement. 
The generalised ensemble method (GEM) and Linear Regression (LR) 
were developed to give more optimal weights to each base model. 
However, both GEM and LR techniques may suffer from a numerical 
problem known as the multi-collinear problem. This problem is a 
consequence of a situation arising where one or more models can be 
expressed as a linear combination of one or more of the other models. 
One approach to ameliorate multi-collinear problems is to use weight 
regularization. An example of this is where the weights are 
constrained to sum to one.

־µ[36]Ϊģ
It has been shown that given the presence of N models it is 
possible that an ensemble learner can perform better if it only uses a 
given subset of those models rather than all [36].

ɼNiall Rooney, David Patterson, Chris NugentPerrone&Cooper
ϸۣȻΪ־ǲͬģǳϮ

Zainal Ahmad, Jie Zhang. Selective combination of 
multiple neural networks for improving model prediction in nonlinear 
systems modelling through forward selection and backward elimination. 
Neurocomputing 72(4-6): 1198-1204, 2009
һߵλΪUniversity Sains MalaysiaڶߵλΪNewcastle 
University

Perrone&Cooperµ[19]ΪģͶ־µ
[30]Ϊģرעǣһġ
Excluding these networks could further improve the generalisation 
capability of the aggregated network. Perrone and Cooper [19] suggest 
a heuristics selection method whereby the trained networks are ordered 
in terms of increasing mean-squared errors (MSE) and only those with 
lower MSE are included in combination. However, combining these 
networks with lower MSE may not significantly improve model 
generalisation since these networks can be severely correlated. Zhou 
et al. [30] show that combining selected networks may be better than 
combining all individual networks and propose a genetic 
algorithm-based approach for selecting individual networks in an 
ensemble. 

Zainal Ahmad, Jie Zhangת۴ʡhowever
Perrone&Cooperº־£ɼΪǲͬġ

ϵ飬ҵóĽ־ĲǳϮ

룬˲ʵָǷҲѧܡ

(XYS20091025)

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˵˵ҵ󺣹꾭

ߣyeslala

Щ죬Ϳʿĺ¼šҲմ˵˵Լ󺣹


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˵ݵķ۵ȷǵ÷ɿ죬ǸСһķ
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ӽˮ֮Ԭ¡ƽ֮

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⻰

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ˮо(IRRI)ӽˮ

ߣHus

AopsҶԡԬ¡ƽӽˮ֮Уѹˮо
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IR8, IR36ȸ߲ˮ֣ʳSwaminathan, Chandler, 
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εᣬͿʿ

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ѰҶ

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Ϊ鲩ʿɱһ

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⼸˿йһ鲩ʿɱۣһⶼж
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ɱԣʲôԿ˷ -- ̸˷

ߣSaijun

ͿʿϢеϧɱԵĻʲôѲԿ˷أ
µǴ۸ʹеĻѧʧȥƽ⣬һ˼ά嶯
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˵ıҪο˷ 1)ҽ鼮Լ
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Ĺס: "Have you ever felt worse after workout?" (from a 
depression website) 3Ȥѧϰ֪ʶ -- ʱʵ
Լ 4һѳȥ档 5)Ҫ𣿸˾һ
εĳߣߣһξͿˡ ƼФ˵ľ꡷
(shawshank redemption)ˡ 

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ûչֿѧ

ߣtiger

ʽÿѧУûչֿѧ
ֻܽһЩ֪ʶѧڲҽͽ
Աһ㣬رһ⣬ȴӲġһЩ
ڻַʵڲȡѧôζĶ
޷ˡʵϲǵģʾ˿ѧĿϲȻϲ
ϲҲûҪϲµǣĳЩͯɻĻ
Ϊʹ˷У˽ѧԿѧȤһɵı֡
ʾȻʧܣʽԭ򡣵һ
ûУǵ纫˲͵ͷ

(XYS20091025)

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ԡٿһơһ

ߣʵڿ

20091020˿վϷˡٿһơд
ǰڡ֪ʶȨ֡վ"(רȨ)˷ٿ"вѯģ
ʱ鵽ٿ3רΪȷԩٿƽڣϡ
֪ʶȨ֡վ"(  )˷ٿơвѯõĽ£

ר (23 )   ʵר (4)  
   ר 
1    200710188333.1     óʽӾγǽǹԶ
(ʵЧ) 
2    200710188332.7     ༶Զⶨ(ʵ
Ч) 
3    200810000478.9     ɭԴֵռϢ (ʵ
Ч)
4    200810000477.4     ɭԴңǹϵͳ
(ʵЧ)  
5    200710188331.2     ֹɽض༶Զλ(ʵЧ) 
6    200410006257.4     ӽǹǼԶ(Ȩ,רȨ
ֹ(δרȨֹ)) 
7    200410090775.9     һֵӾγǲϵͳ(ʵ
Ч  )
8    200410006258.9     ƵվǼɭּƲⷽ(Ȩ,רȨֹ
(δרȨֹ)) 
9    200510097974.7     ɭ̲Ǽʹ÷( Ȩ) 
10    200510127584.X     ֽ綨λǼʹ÷( ר빫
Ϊ) 
11    200610109259.5     һֻ𳡵ʵʱ (ʵЧ)
12    200610150084.2     չάɵĵľ֦Ŀ
㷨(ר빫Ϊ) 
13    200610000856.4     άɨϵͳľĻ( ʵ
Ч) 
14    200610000857.9     ˻ӰңɭּƲⷽ( Ȩ) 
15    200610145564.X     ͨ(ר빫
Ϊ) 
16    200710001104.4     ɭάԶط( Ȩ) 
17    200710001105.9     ȫվ߷(ʵЧ) 
18    200610145563.5     ֳʽGPSջֱӲⶨϵƽֱ
귽( ʵЧ) 
19    200710086504.X     ֻܱ淽( ʵЧ) 
20    200810132915.2     һϾγǲ(ʵ
Ч) 
21    200810132918.6     һӰ﷽Ƹ˼ʹ÷(ʵ
Ч) 
22    200810126380.8     һͨǻγǲľ콻ȷ
(ʵЧ) 
23    200810126381.2     һֲͨӵĵ˼ʹ÷(ʵ
Ч) 
24    200420112647.5     Ƶվ(Ȩ,רȨֹ(δר
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25    200420112649.4     һ͵ӽǹ(Ȩ,רȨֹ(δ
רȨֹ)) 
26    200420112648.X     ͵Ӿγ(Ȩ,רȨֹ(δ
רȨֹ))  
27    200520107092.X     ɭ̸Ľǹ(Ȩ,רȨ
ֹ(δרȨֹ)) 

Ŀǰٿƽ2200510097974.7ɭ̲Ǽʹ÷
( Ȩ)200710001104.4ɭάԶط( Ȩ) Чķר
ش˸͸ٿƽڼǸ

(XYS20091025)

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顪ڸ￵һ

ߣչ

￵ŵгմ֢Ϣͬʱϲӣ
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ƽĳɹʱȻͻ֢һʮꡣֱ˲ɺɺ٣
ӵİ۲Ŭ£ʲָ1994ŵݽ̨ʱ
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ʮǸĿʱ̲⣬콱̨һǵ
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ѿ鷢Ĺ

Ǹ̾˵һŵͺˣһýǻ
ԾӦõӸ￵Ĺȫû⡣ŵ
ĸ￵ýɣĹй⴫Ĺͨ
Ѷʱ֡ͼ֡Ӱ˲䴫ȫһǰǹϵ
ʵŸ￵ŵĵˡΪʲôȥ겻
ӦǼλԱȨĽȥҲ˵ýо
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廹ھ޴ӦüֵϣӦù㷺CCDڽǾһ
ǵġǾǵܰʷһ꣬Ҳձ˻񽱡
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Ҫ̸̸մ֢ˡǵǰͳҲǴ˲ߡմ֢ʲôԭ
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Ԥϣֻʽ״̬ı䡣ʲôʽԤ
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룬Ҳһ֡ʵ顱ܸ˴һʾһ
֪ŴԳմϣԤƳٳմ֢סĪͳ

ûеͳУ19871996ľ䵣Ĵ
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صĶں档ͬ10£д30У죬ٰʢ
ġաףʱѧ֯޸ƽǱִҪУŸɡ
ڿ죬д󲻵ϲ󣬸ӹڸƼУҪ´ǵ
ʱ䲻ѧ̨ڶαҳͬѧԼУѵ
Ŀ֮£ҪУеġ䡱(ָͲע)Ա
پд˥ࡱĺ̨һţ
֮ã˾д󶪾ӡѧּкɷڲش
д̴ѧ󣬼Ƶ¡(ԲǿУĹ
 ѧǰݡ)

Ȼȷҡĸĥ˵˶áˣûм֮࣬
￿ǵһֳô᲻ɼľ̼ڴҶ
֪￵ĳմ֢ŴأǲдЩ̼
ĺȻһ֪첻ˣβӦ¶Щ
Ĵ̼С룬ûȥдУŵ
У̼״̬ҲںһЩʲ
Ͻ̨һ˵˵

￱ϾǸ￣˿ͳս°ӡ±´
̨ʱһλУͷѧһ˵ͷΪ
ʲôҪͷѧտһŹ顣(2009.10.22)

(XYS20091025)

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Ŀϵ¼Ϊʲôóԣ

ߣMrMathematica

߶ʯڣлʳмµĿܣһ˵Ŀϵ¼
ըͱصĸóԣDWҲĹ۵㡣Ϊ˵
ĶǴģҵĿ

ȣóһ۵ԣһȫ͹۵ָܹ˵
һ֣һЩɷ־һһֺóԡһ˵ӣ˾÷
ԣʺһúóԡκγʦرʳƷ꣬
Ϊȷһ䷽óԡһЩдԵȺܹ
ǱڿͻȺԳԲ֣Ȼѡȡߵ䷽ģƹ㡣˵ˣ
ǲcareһ䷽óԡǸעش󲿷ֵ˻ϲһ
䷽ϻǮϵ¼Ĵ꣬ڽй֮ǰ
϶оǣйΪйĿϵ¼ĺóԣ
ΪĿϵ¼йĺóԡ

ûʲôֵģ׶ѡپһӣв͹ݣһ
㶼ײ˵һ˵Ŀζģһй˵Ŀζ
ġȥ˲͹ݣҸȡˡֵǣΪ˿Ķ
ߣȻ붫ôۣʵ飬뵱ȻΪԼĿ
Եģȫ۵ĺóԵ͹۵ۣ˼˿
Ƿһֱϣһĵģд˲ٹʳƷ
£ܸߣȤȥ

(XYS20091025)

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Ҳ˵˵ΪʲôĿϵ»óԼ

ߣSongbie

2002ʱͬ˵Ŀϵ»ȺǵĺóԵĶࡣ
ǳ棺ųͳһ׼ֲмʮ󱾵Ŀϵ»ôв
أҺע۲ϵ»ȥʱ򣬸ΪɬԿ
»;ѡ˵滹һѧԭİ¶δ
֪Ŀ־˸ƫѡϤҲǿҵĸоĿϵ»ȷʵ
籱ĺóԡ

ԭȫͬ09.10.23, DWĿϵ¼Ϊʲôó?е
۵㣺й˶࣬ãԶһ¯͵ǵͣԸо
ɿڣ壬ճԵĶӲζͲ˺ܶࡣ

ҹעϵ»֮һǳǿҵĸоǣϵ»ҼĲ
ҵߴԵġÿ꣬ϵ»ƳܶµʳƷ档
һ꣺ϺĴţ巽зЩƷ
ݿͻķʧĿ¼УĻ
ҪڵĿ¼ǰĶԱһ£ͻֱᷢ仯
󡣣Ҳһйϵ»óԵԭΪƷḻЩ

ϵ»ʱںܶη̼ϣϵ»Ͱײд
Ĭϵϻɹ֭ˡ

۲Ҹдͦģһ͵ҵͳĶٴͳˣ
ôⴴ£ֻʹԼǿ

ǿ˵ϵ»йЩ¿ܶй˰ġ
ƺҲô򵥡жлʳųҪйĿϵ»Ľأ
Ҷü

ȫ09.10.22, ʯлʳмµĿܡе
۵㣬лʳȫûмµĿܣҲϵ»һ
д¾һʱ

(XYS20091025)

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ϵ»ʽԪ

ߣʯ

DWը۳ʹʳƷһܵĺ,
Ƿں,Ҳö֪ϵ»(ǻǼ!)ը
,Ҳö֪Լļͥʵ,˿¡ը
ʱ,ը֮ǰȳ򡣿ϵ»Ӧǹ淶ĳ,
йǷйɫӦ洫
޵θڡ,ǲö֪,ڱĿϵ»Ƥ
ȶʽʳƷƷȻйˡв͵Ʒֺ()
ƴࡰ֪ʶȨ,˽ԿѡáЩöֻ֪ܵ
ҵʿæˡ

(XYS20091025)

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ϴѧһλ鲩ʿ潭ɱ һ

    1023Ѷ գϴѧһ鲩ʿ潭ɱѧУ
Ϊ׷ᡣ

    ĳУ197051ոս顣˽⣬ϵ˲ŵ
ʽϴѧϢ̹ѧԺģн1Ԫһꡣ

    ѧ˶ʿҵĳѧȺô
⹥˲ʿѧλ

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