Machine Learning. Hidden Markov Model. Eric Xing / /15-781, 781, Fall Lecture 17, March 24, 2008

Transcription

1 Mache Learg 0-70/5 70/ Fall 2008 Hdde Marov Model Erc Xg Lecure 7 March Readg: Cha. 3 C.B boo Erc Xg Erc Xg 2

2 Hdde Marov Model: from sac o damc mure models Sac mure Damc mure Y Y Y 2 Y 3 Y X N X X 2 X 3 X Erc Xg 3 Hdde Marov Models he uderlg source: geomc ees dce he sequece: Y X Y 2 Y 3 X 2 X 3 Y X Plo N sequece of rolls Erc Xg 4 2

3 Eamle: he Dshoes Caso caso has wo dce: Far de P P2 P3 P5 P6 /6 Loaded de P P2 P3 P5 /0 P6 /2 Caso laer swches bac-&-forh bewee far ad loaded de oce ever 20 urs Game:. You be $ 2. You roll alwas wh a far de 3. Caso laer rolls mabe wh far de mabe wh loaded de 4. Hghes umber ws $2 Erc Xg 5 Puzzles Regardg he Dshoes Caso GIVEN: sequece of rolls b he caso laer QUESION How lel s hs sequece gve our model of how he caso wors? hs s he EVLUION roblem HMMs Wha oro of he sequece was geeraed wh he far de ad wha oro wh he loaded de? hs s he DECODING queso HMMs How loaded s he loaded de? How far s he far de? How ofe does he caso laer chage from far o loaded ad bac? hs s he LERNING queso HMMs Erc Xg 6 3

4 Sochasc Geerave Model Observed sequece: B Hdde sequece a arse or segmeao: B B B Erc Xg 7 Defo of HMM Observao sace lhabec se: Eucldea sace: C { c c 2 L c K } d R Ide se of hdde saes I { 2 LM} raso robables bewee a wo saes j a or j ~ Mulomal a a 2 K a M I Sar robables ~ Mulomal π π 2 K π M. Emsso robables assocaed wh each sae or geeral:. ~ Mulomal b b K b. 2 K I θ. ~ f I Grahcal model K 2 Sae auomaa Erc Xg 8 4

5 Probabl of a Parse Gve a sequece ad a arse o fd how lel s he arse: gve our HMM ad he sequece Jo robabl P π M Margal robabl: Poseror robabl: def M + j j [ j ] + def def Le π [ ] a a π ad b [ ] b a La 2 b Lb Erc Xg 9 M K L π a 2 N 2 / he Dshoes Caso Model FIR LODED P F /6 P2 F /6 P3 F /6 P4 F /6 P5 F /6 P6 F / P L /0 P2 L /0 P3 L /0 P4 L /0 P5 L /0 P6 L /2 Erc Xg 0 5

6 Eamle: he Dshoes Caso Le he sequece of rolls be: he wha s he lelhood of Far Far Far Far Far Far Far Far Far Far? sa al robs a 0Far ½ a oloaded ½ ½ P Far PFar Far P2 Far PFar Far P4 Far ½ / Erc Xg Eamle: he Dshoes Caso So he lelhood he de s far all hs ru s jus OK bu wha s he lelhood of π Loaded Loaded Loaded Loaded Loaded Loaded Loaded Loaded Loaded Loaded? ½ P Loaded PLoaded Loaded P4 Loaded ½ /0 8 / herefore s afer all 6.59 mes more lel ha he de s far all he wa ha ha s loaded all he wa Erc Xg 2 6

7 Eamle: he Dshoes Caso Le he sequece of rolls be: Now wha s he lelhood π F F F? ½ / same as before Wha s he lelhood L L L? ½ /0 4 / So s 00 mes more lel he de s loaded Erc Xg 3 hree Ma Quesos o HMMs. Evaluao GIVEN a HMM M ad a sequece FIND Prob M LGO. Forward 2. Decodg GIVEN a HMM M ad a sequece FIND he sequece of saes ha mamzes e.g. P M or he mos robable subsequece of saes LGO. Verb Forward-bacward 3. Learg GIVEN a HMM M wh usecfed raso/emsso robs. ad a sequece FIND arameers θ π a j η ha mamze P θ LGO. Baum-Welch EM Erc Xg 4 7

8 lcaos of HMMs Some earl alcaos of HMMs face bu we ever saw hem seech recogo modellg o chaels I he md-lae 980s HMMs eered geecs ad molecular bolog ad he are ow frml ereched. Some curre alcaos of HMMs o bolog mag chromosomes algg bologcal sequeces redcg sequece srucure ferrg evoluoar relaoshs fdg gees DN sequece Erc Xg 5 cal srucure of a gee Erc Xg 6 8

9 GENSCN Burge & Karl 5'UR Forward + srad Reverse - srad E0 E E2 I 0 I I 2 E romoer E s ergec rego E ol- 3'UR Forward + srad Reverse - srad θ θ θ θ GGCGGGGGGGGCGCCGCGCCGC CGGGCCCGCGCCGCCGCGCCGG GGCGCCCGCCGCC GCGCCCCGCCGGCCCCCC CCGCGCGGCGGGGGCGCC CGGCCGGCCCGCGCGCGC GCGCGGCGCGCG GCGGCCGCCGGGGCGCCGCGCCC GCGCCCGGCCCCCCGCCGGC CCGGCGGGCGGCCGCGGCGGCGCC GGGGGGGCGCGGCCGCGCGCCG CGGCGGGCGCGGGCCGCCCC GCGGGGGCCCGCCCCGCG GGCGGGCCCCG CGGCGGGCGCCGCGGGC CCCGGGCCCGCCCGCGCGCCC GCGGCCGCGCCCCCCCCCCG CCCCGCCCGCCGCCCCCCCG GCCGCGGGCGGCGCGGGCCGC GGGCGCGCCGCC CGGGCGCCCGG GGGCGGCCGCGG GCGGGCCCGG GGGCGCCGG GGGCGCGGGGCGGCCG CCGGCGGCGG GGCGCGCCGGGCGC GGGGCCCGGGGCGC GGGCGGGCGGGGC GCCCCCCG GCCCCGGCCGG GGCGCGGCGG CCGCGGGCCGGCGG CCCCGCGCGCCGCGCC GCGCCGCGC CCGGCCC CCCCGCCGGGCGCCC CCGGGGGCGCCCCGGG GCGGCCGGCGCCCCGCGG Erc Xg 7 he HMM lgorhms Quesos: Evaluao: Wha s he robabl of he observed sequece? Forward Decodg: Wha s he robabl ha he sae of he 3rd roll s loaded gve he observed sequece? Forward- Bacward Decodg: Wha s he mos lel de sequece? Verb Learg: Uder wha arameerzao are he observed sequeces mos robable? Baum-Welch EM Erc Xg 8 9

10 he Forward lgorhm We wa o calculae P he lelhood of gve he HMM Sum over all ossble was of geerag : 2 N 2 L π a o avod summg over a eoeal umber of ahs defe α def α P he forward robabl he recurso: α P α α a Erc Xg 9 he Forward lgorhm dervao Comue he forward robabl: - α P P P P P P P P P P P P α a - Cha rule : P B C P P B C P C B Erc Xg 20 0

11 he Forward lgorhm α We ca comue for all usg damc rogrammg! Ialzao: Ierao: α P π α P P P P π α P α a ermao: P α Erc Xg 2 he Bacward lgorhm We wa o comue P he oseror robabl dsrbuo o he h oso gve We sar b comug P P + P P + P P he recurso: Forward α Bacward β a + + β + β P + Erc Xg 22

12 he Bacward lgorhm dervao Defe he bacward robabl: + β P + P P P P P β a Cha rule : P B C α P α P B C α P C B α Erc Xg 23 he Bacward lgorhm β We ca comue for all usg damc rogrammg! Ialzao: β Ierao: β β ermao: a P P α β Erc Xg 24 2

13 Poseror decodg We ca ow calculae P α β P P P he we ca as Wha s he mos lel sae a oso of sequece : * Noe ha hs s a MP of a sgle hdde sae wha f we wa o a MP of a whole hdde sae sequece? Poseror Decodg: arg ma P hs s dffere from MP of a whole sequece of hdde saes P hs ca be udersood as b error rae vs. word error rae Eamle: MP of X? MP of X Y? 0.3 Erc Xg 25 * { : } L Verb decodg GIVEN we wa o fd such ha P s mamzed: Le V * argma P argma π P ma } P - - { - Probabl of mos lel sequece of saes edg a sae he recurso: N Sae 2 V ma a V Uderflows are a sgfca roblem K K π a La b Lb K 2 hese umbers become eremel small uderflow Soluo: ae he logs of all values: log V log a + V V + ma Erc Xg 26 3

14 he Verb lgorhm dervao Defe he verb robabl: V + ma } P + + { ma { } P + + ma } P P { P ma P + ma{ } P ma P + + a V P + + ma a V Erc Xg 27 he Verb lgorhm Iu: Ialzao: V Ierao: V P ma a V * P ma V racebac: P π Pr arg ma a V ermao: * arg ma V * * Pr Erc Xg 28 4

15 Comuaoal Comle ad mlemeao deals Wha s he rug me ad sace requred for Forward ad Bacward? α α a β a + + β+ V ma a V me: OK 2 N; Sace: OKN. Useful mlemeao echque o avod uderflows Verb: sum of logs Forward/Bacward: rescalg a each oso b mullg b a cosa Erc Xg 29 Learg HMM: wo scearos Suervsed learg: esmao whe he rgh aswer s ow Eamles: GIVEN: GIVEN: a geomc rego where we have good eermeal aoaos of he CG slads he caso laer allows us o observe hm oe eveg as he chages dce ad roduces 0000 rolls Usuervsed learg: esmao whe he rgh aswer s uow Eamles: GIVEN: GIVEN: he orcue geome; we do ow how freque are he CG slads here eher do we ow her comoso 0000 rolls of he caso laer bu we do see whe he chages dce QUESION: Udae he arameers θ of he model o mamze P θ --- Mamal lelhood ML esmao Erc Xg 30 5

16 Suervsed ML esmao Gve N for whch he rue sae ah N s ow Defe: j # mes sae raso j occurs # mes sae ems B We ca show ha he mamum lelhood arameers θ are: a ML j b ML # j # # # j 2 2 Homewor! Wha f s couous? We ca rea : : : N as N observaos of e.g. a Gaussa ad al learg rules for Gaussa Homewor! Erc Xg 3 j j ' ' B B j ' ' { } Suervsed ML esmao cd. Iuo: Whe we ow he uderlg saes he bes esmae of θ s he average frequec of rasos & emssos ha occur he rag daa Drawbac: Gve lle daa here ma be overfg: P θ s mamzed bu θ s ureasoable 0 robables VERY BD Eamle: Gve 0 caso rolls we observe F F F F F F F F F F he: a FF ; a FL 0 b F b F3.2; b F2.3; b F4 0; b F5 b F6. Erc Xg 32 6

17 Pseudocous Soluo for small rag ses: dd seudocous j B # mes sae raso j occurs + R j # mes sae ems + S R j S j are seudocous rereseg our ror belef oal seudocous: R Σ j R j S Σ S --- "sregh" of ror belef --- oal umber of magar saces he ror Larger oal seudocous srog ror belef Small oal seudocous: jus o avod 0 robables --- smoohg Erc Xg 33 Usuervsed ML esmao Gve N for whch he rue sae ah N s uow EXPECION MXIMIZION 0. Sarg wh our bes guess of a model M arameers θ:. Esmae j B he rag daa j How? j B How? homewor 2. Udae θ accordg o j B Now a "suervsed learg" roblem 3. Reea & 2 ul covergece hs s called he Baum-Welch lgorhm We ca ge o a rovabl more or equall lel arameer se θ each erao Erc Xg 34 7

18 8 Erc Xg 35 he Baum Welch algorhm he comlee log lelhood he eeced comlee log lelhood EM he E se he M se "smbolcall" decal o MLE c 2 log log ; θ l + + j j c b a 2 log log log ; θ π l γ j j j ξ j ML j a 2 γ ξ ML b γ γ N ML γ π Erc Xg 36 he Baum-Welch algorhm -- commes me Comle: # eraos OK 2 N Guaraeed o crease he log lelhood of he model No guaraeed o fd globall bes arameers Coverges o local omum deedg o al codos oo ma arameers / oo large model: Over-fg