[业务]机器学习-想也许方法

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Linear Discriminant Analysis锛氳繖涓 綘鍙 渶瑕佺煡閬撲粈涔堟槸澶氬彉閲廏aussian鍒嗗竷銆? Logistic Regression锛氬 鏋滅煡閬撶嚎鎬у洖褰掑拰骞夸箟绾挎?у洖褰掞紝LR涔熶笉鏄 粈涔堢壒鍒 殑涓滆タ銆傚 鏋滅煡閬撴渶澶х喌鍘熺悊锛屽苟鑳戒粠瀹冩帹瀵煎嚭LR閭, 鏄庝綘瀵筁R鐨勭悊瑙,張鏇存繁鍏ヤ簡銆? Linear SVM锛氳繖涓 ?寰 鏉備竴鐐癸紝鍥犱负闂 鐨刦ormulation闇?瑕佸厛鐞嗚Вmax-margin鍘熺悊銆傝?屽叿浣撶殑绠楁硶瀹為檯涓婂氨鍙 槸缁忓吀鐨勪簩娆? 鍒掑拰鍑镐紭鍖栧唴瀹广?? Kernel SVM锛氳 鐪熸 鐞嗚В杩欎釜鎴栬 闇?瑕佸厛鏄庣櫧浠?涔堟槸RKHS銆傜劧鍚庡叾浠栫畻娉曢儴鍒嗗彧鏄 豢鐓 inear SVM鐨勭畝鍗曟帹骞裤?俁KHS鐩稿叧鍐呭 鍙 互鍙傜发 umiacs.umd.edu/~hal/doc Adaboost锛氳繖涓 笢瑗垮 鏋滃彧闇?瑕佺煡閬撶畻娉曡繃绋嬶紝鏄 緢绠?鍗曠殑涓滆タ銆備絾鏄 鏋滀綘鑳芥槑鐧戒负浠?涔堣繖涔堝仛锛屽湪浠?涔堝亣璁句笅杩欎箞鍋氫細鏀舵暃鍒版渶浼樿В锛岄偅浣犵殑鐞嗚В涔熼潪甯镐笉閿欎簡銆? Decision Tree锛氭湁涓や釜闇?瑕佷簡瑙 ART 鍜?c4.5銆傝繖涓 緢绠?鍗曪紝娌′粈涔堝ソ璇寸殑锛屼絾鏄 綘鑳戒笉鑳介珮鏁堢殑瀹炵幇瀹冧滑鍛 紵 Neural network锛氳繖涓 槸鎴戣 杩囨渶鍌荤殑妯″瀷锛屼綘瑕佺煡閬撴?庝箞鍋氫紭鍖栵紝涔冭嚦鎬庝箞鍋氶殢鏈轰紭鍖栵紝缁撴灉鐪嬪ぉ鍚冮キ銆? 鍐嶆潵鐪嬩竴浜涢潪鐩戠潱妯″瀷锛屾瘮濡傜粡鍏哥殑鏈? 鏁版嵁澶勭悊涓庡彲瑙嗗寲锛歅CA锛孡DA锛孧DS锛屼互鍙婂叾浠栤?滈珮澶т笂鈥濅絾涓嶄竴瀹歸ork鐨刴anifold learning绠楁硶 鑱氱被绠楁硶锛屼互鍙婂 浣曡瘎浠疯仛绫荤粨鏋? 绋?鐤忕紪鐮侊細濡備綍鎶婁竴涓 甫LASSO鐨勯棶棰樿浆鍖栨垚绾挎?х害鏉燂紵鏈夊摢浜涘埆鐨勬洿蹇 殑鏂规硶姹傝ВLASSO銆? 浠ヤ笂杩欎簺涓滆タ锛岀畻鏄 叆闂ㄦ?ц川鐨勩?傛湰绉戞瘯涓氬悗澶ф 涓?骞村乏鍙筹紝杩欎簺涓滆タ鎴戝氨鍩烘湰鐔熸倝浜嗐?傝 瀛，範杩欎簺涓滆タ锛岀湅涓?浜涙暀鏉愯嚜鐒舵槸濂界殑锛屼 絾鏄 功閲屽簾璇濇瘮杈冨 鍛 紝鑰屼笖涓?鏈 功鐨勪綔鑰呯煡閬撶殑涓滆タ姣曠珶鏈夐檺锛屾垜閮芥槸鍊惧悜鐩存帴浠庣淮鍩哄嚭鍙戞壘璧勬枡鐪嬬殑銆傝 瀹炶瘽锛岀幇鍦ㄥ緢灏戜細鑷 繁鍘诲疄鐜拌繖浜涚畻娉曚簡锛岃繖浜涚粡鍏哥畻娉曢兘鏈夌幇鎴愮殑寮?婧愬伐鍏枫?備簨瀹炰笂瑕佸啓涓?涓 珮鏁堢殑Linear SVM涔熶笉鏄 緢瀹规槗鐨勪簨鎯呫?? 鎴戜富瑕佽 璁插 瀹岃繖浜涳紝搴旇 鎬庝箞瀛,洿楂樼骇鐨勫唴瀹癸紝褰撶劧杩樻槸缁撳悎鎴戣嚜宸辩殑缁忓巻銆傛巿浜轰互楸间笉濡傛巿浜轰互娓旓紝瑕佸 涔犲墠娌跨殑鍐呭 灏辫 鎺屾彙鍩虹 鐨勫伐鍏枫?備功鍒嗕负涓ょ 锛屼竴绉嶄功鐪嬪畬浜嗗氨鏄 湅瀹屼簡锛屼綘瀛，埌浜嗕竴鍫嗘妧鑳斤紝浣嗗嵈涓嶈兘鐢ㄨ繖浜涙妧鑳戒骇鐢熸柊鐨勭煡璇嗭紝闈? 闂 涔熶笉鑳藉洜鍦板埗瀹滐紝濡傛灉鏄 繖鏍峰 浼拌 鍙 兘鐢ㄦ潵搴斾粯鎵惧伐浣滈潰璇曞惂锛涘彟涓?绉嶄功鐪嬪畬浜嗘墠鏄 涔犳柊涓滆タ鐨勫紑濮嬶紝浣犲 鍒颁簡濡備綍璇绘噦鍒 汉鐨勮 鏂囷紝濡備綍寮?鍙戞柊鐨勭煡璇嗭紝濡備綍鏍规嵁鎯呭喌閫夋嫨鍜岃皟鏁寸畻娉曘?? 姒傜巼鍥炬ā鍨嬶紙Probabilistic graphical model锛夛細鎴戞槸鍦–oursera涓婂 涔犳 鐜囧浘妯″瀷杩欓棬璇剧殑锛岃 寰楃湡鐨勯潪甯稿ソ锛屾 鎵撶畻杩囦簩鍛ㄧ洰銆傚 瀹岃繖涓 锛屾帉鎻′簡鍥炬ā鍨嬬殑璁捐 锛屾帹鏂 紝鍜岄噰鏍锋柟娉曚箣鍚庯紝灏卞彲浠ュ紑濮嬪 涔犱袱涓 牳蹇冪殑鏈哄櫒瀛，範妯″瀷锛屼竴涓 槸Latent Dirichlet Allocation锛圠DA锛夛紝甯哥敤浜庢枃鏈 鐞嗭紱涓?涓 槸Probabilistic Matrix Factorization锛圥MF锛夛紝甯哥敤浜庢帹鑽愮郴缁熴?? 鏈変釜鏈夎叮鐨勪簨鎯咃紝鏌愬勾Siggraph涓婃湁涓?绡囨枃绔犲仛鐨勬槸鑷 姩鐢熸垚缇庝附鐨勫浘妗堜笂鑹诧紝杩欑瘒鏂囩珷绠? 鐩存槸鎴戠殑鏈?鐖卞晩锛屼綔鑰呮槸涓 崕瑁斿 濮愰暱寰椾篃寰堣悓 >_< graphics.stanford.edu/~ 鍏跺疄鏄 负浜嗙湅鎳傚ス鐨勫伐浣滐紝鎴戞墠鍘诲 鐨勮繖闂ㄨ 鍛 ?? 杩欑被鍥炬ā鍨嬮兘鍦ㄧ爺绌朵袱涓 熀鏈 殑闂 锛氬 浣曢噰鏍凤紱濡備綍inference闅愬惈鍙橀噺锛屾槸鐢‥M銆丮CMC銆佽繕鏄疺ariational Bayes锛屼粠鑰岀敤鏉ヤ及绠楀弬鏁般?備负浜嗘悶娓呮 杩欎簺锛屽 涔犱笅闈?繖闂ㄨ 灏遍潪甯稿繀瑕佷簡銆? 缁熻 璁,畻锛圫tatistical computing锛夛細杩欎釜璇剧郴缁熺殑浠嬬粛浜嗘暟鍊肩Н鍒嗘柟娉曪紝Monte Carlo鏂规硶锛坕mportance sampling, MCMC锛孲equential/Particle MCMC锛宐ootstrap锛夛紝EM/MM銆傚 瀹岃繖闂ㄨ 锛屼綘鑳藉 杩欎釜棰嗗煙鐨勫伐鍏锋湁涓 取灞?鎬х殑浜嗚В锛屾槑鐧芥瘡涓 伐鍏风殑鍒?紛锛屽畠浠 骇鐢熺殑鍘嗗彶鏉ユ簮锛屼粠鑰屽湪鍏蜂綋闂 涓 纭 殑閫夋嫨浣跨敤瀹冧滑銆? 鏈変簡杩欎簺宸ュ叿锛屼綘浼氬彂鐜板ぇ閮ㄥ垎research鐨勫伐浣滈兘鏄 湪杩欎簺缁嗗垎棰嗗煙鍋氫竴浜涚畝鍗曠殑鎵?睍銆傛瘮濡侭ayesian PMF锛圛CML鈥?8锛夎繖绡囨枃绔犲嚑涔庡氨瀹屽取鏄疢CMC鍦?MF鐨勫簲鐢ㄣ?傝瘽璇村洖鏉ワ紝Research澶ч儴鍒嗘椂鍊欏氨鏄 互杩欐牱涓?绉嶅惊搴忔笎杩涚殑鏂瑰紡杩涜 鐨勶紝鎶婁竴浜涚幇鎴愮殑idea鎺掑垪缁勫悎銆? 娣卞害瀛，範锛圖eep learning锛夛細璇村疄璇濇垜鍒氬紑濮嬫帴瑙，繖鍧楀唴瀹瑰彂鐜帮紝杩欏凹鐜涘氨鏄 紶璇翠腑鐨勯粦绉戞妧鍟娿?備綘涓嶇煡閬撴ā鍨嬮噷闈?彂鐢熶簡浠?涔堬紝濂藉潖閮芥槸鐪嬪ぉ鍚冮キ鐨勬劅瑙夈?備负浜嗘悶娓呮 杩欎釜锛屾垜鍐冲畾閲嶅ご寮?濮嬪疄鐜扮 缁忕綉缁溿?傦紙浠,爜鍦?obye/neuron 路 GitHub 锛夊墠鍓嶅悗鍚庤姳浜嗚繎鍗婂勾鐨勬椂闂达紝鍦ㄥ疄鐜扮殑杩囩?涓 紝鎴戝 涔犱簡鏋勯?犲拰璁 粌绁炵粡缃戠粶鐨勫悇涓 粏鑺傘?傛垜鏄 粠Stanford杩欎釜Tutorial寮?濮嬪 涔犵殑UFLDL Tutorial 璇剧?璧勬枡閲屾彁渚涗簡Matlab鐨勬簮鐮侊紝涓嶈繃鎴戝枩娆?噸鏂伴?犺疆瀛愶紝閭,釜鏃跺?欐伆濂藉湪瀛，範Scala锛屽氨鐢?cala閲嶅啓浜嗕竴涓 缁忕綉缁滅殑搴擄紙杩欎釜璇 ?鐨勭壒鎬ч潪甯搁?傚悎鍐欑 缁忕綉缁滅殑绠楁硶锛夈?? 杩戝嚑骞存繁搴， 涔犵殑涓绘祦琚 繁搴，嵎绉 綉缁滀唬鏇匡紝杩欑 鐩戠潱瀛，範鐨勭畻娉曡櫧鐒跺 鏌愪簺闂 鍗佸垎鏈夋晥锛屼絾鏄 暟瀛，笂骞朵笉鏄 壒鍒 濂囩殑涓滆タ锛屾垜杩樻槸姣旇緝鍏虫敞閭,簺闈炵洃鐫,殑绁炵粡缃戠粶銆? 浼樺寲锛坥ptimization锛夛細娌?湁浼樺寲绠楁硶锛屼换浣曟満鍣ㄥ 涔犳ā鍨嬮兘鏄 ?涓 ゼ闃侊紝濡備綍鐢ㄦ洿楂樻晥鐨勪紭鍖栫畻娉曪紝濡備綍trade-off 璁,畻鏃堕棿鍜屽噯纭 害锛屽 浣曟妸宸叉湁闂 scale鍒版洿楂樿 妯,殑鏁版嵁涓婁竴鐩撮兘鏄 ?滀紭鍖栧ぇ甯堜滑鈥濆仛涓嶅畬鐨勫伐浣溿?傝繖涔熸槸涓?涓 潪甯稿ぇ鐨勫垎鏀 紝鎴戣 寰楃幇鍦ㄦ瘮杈冩祦琛岀殑涓や釜澶х被鏄 殢鏈烘 搴，紭鍖栧拰ADMM銆傚墠鑰呯敤鏉ヨВ鍐冲ぇ瑙勬ā闈炵害鏉熶紭鍖栭棶棰橈紝鐜板疄鎯呮櫙鐢ㄧ殑寰堝 锛屼絾鎴戜滑瀵瑰畠鐭ラ亾鐨勫緢灏戯紱鍚庤?呯敤鏉ヨВ鍐冲甫绾,潫闂 锛屾湁寰堝 鍙樹綋銆傛 澶栵紝浼樺寲澶у 搴 篃鍙堟湁寰堝 鍒 殑鎴愬憳锛岃繖鏃跺?欐垜瑕佹帹鑽愮殑璧勬枡鍖呮嫭J Nocedal鐨刵umerical optimization杩欐湰涔,紝璁茬殑鍐呭 闈炲父鍏呭疄銆傛 澶朅DMM鐨勫唴瀹瑰綋鐒剁湅Boyd宸ㄧ墰11骞寸殑Tutorial paper銆? 璇濊 鈥滄 鐜囧浘妯″瀷鐢诲湀锛岀 缁忕綉缁滆皟鍙傛暟锛屼 紭鍖栭棶棰樺姞姝,垯鈥濓紝涓嶄細绉戠爺涔熶細鑳?瘜浜嗐?傘?傘?傛渶鍚庤 璇存渶杩戝湪鐪嬬殑涓滆タ銆侾AC瀛，範鐞嗚 锛圥AC Learning锛夛細杩欎釜鐞嗚 宸茬粡鐩稿 鍙よ?佷簡锛屽畠鐨勫巻鍙蹭环鍊煎緢澶э紝搴旂敤浠峰?煎緢鏈変簤璁 紝浣嗘槸涓?鐩存湁浜哄湪缁х画杩欎釜鏂瑰悜鐨勫伐浣滐紝骞惰瘯鍥剧敤瀹冩潵鏋勯?犳柊鐨勬ā鍨嬶紝鎵?浠ヨ繕鏄 湁蹇呰 鐭ラ亾鐨勩?傛帹鑽愪竴涓嬫渶杩戠殑鏂颁功锛歎nderstanding Machine Learning锛?From Theory To Algorithms. 闈炲弬鏁拌礉鍙舵柉缁熻 锛圢on-parametric Bayesian statistics锛夛細杩欎釜鏂瑰悜杩橀潪甯稿勾杞伙紝鏈夊緢澶氶渶瑕佹寲鎺樼殑涓滆タ锛屼篃鏄疨hD鐨勪竴涓 噸瑕佽 棰樸?