{"id":998,"date":"2023-03-20T13:17:27","date_gmt":"2023-03-20T10:17:27","guid":{"rendered":"https:\/\/legendhomework.com\/tr\/?p=998"},"modified":"2023-03-20T13:17:27","modified_gmt":"2023-03-20T10:17:27","slug":"radyal-temel-fonksiyon-aglari-multimedya-bolumu-multimedya-bolumu-odevleri-multimedya-bolumu-tez-yaptirma-multimedya-bolumu-odev-ucretleri","status":"publish","type":"post","link":"https:\/\/legendhomework.com\/tr\/radyal-temel-fonksiyon-aglari-multimedya-bolumu-multimedya-bolumu-odevleri-multimedya-bolumu-tez-yaptirma-multimedya-bolumu-odev-ucretleri\/","title":{"rendered":"Radyal Temel Fonksiyon A\u011flar\u0131 \u2013 Multimedya B\u00f6l\u00fcm\u00fc \u2013 Multimedya B\u00f6l\u00fcm\u00fc \u00d6devleri \u2013 Multimedya B\u00f6l\u00fcm\u00fc Tez Yapt\u0131rma \u2013Multimedya B\u00f6l\u00fcm\u00fc \u00d6dev \u00dccretleri"},"content":{"rendered":"

Radyal Temel Fonksiyon A\u011flar\u0131<\/span><\/strong><\/h3>\n

Di\u011fer bir ileri beslemeli a\u011f t\u00fcr\u00fc, radyal temel fonksiyon (RBF) a\u011f\u0131d\u0131r. Gizli katmandaki her n\u00f6ron, aktivasyon i\u015flevi olarak hizmet etmesi i\u00e7in bir RBF (\u00f6rne\u011fin bir Gauss \u00e7ekirde\u011fi) kullan\u0131r.<\/span><\/p>\n

RBF a\u011f\u0131ndaki a\u011f\u0131rl\u0131kland\u0131rma parametreleri, bu \u00e7ekirdeklerin merkezleri, geni\u015flikleri ve y\u00fckseklikleridir. \u00c7\u0131kt\u0131 i\u015flevleri, bu RBF’lerin do\u011frusal birle\u015fimidir (\u00e7ekirdeklerin y\u00fckseklikleriyle a\u011f\u0131rl\u0131kland\u0131r\u0131lm\u0131\u015ft\u0131r). RBF a\u011f\u0131n\u0131n bir MLP ile ayn\u0131 evrensel yakla\u015f\u0131m g\u00fcc\u00fcne sahip oldu\u011fu g\u00f6sterilmi\u015ftir.<\/span><\/p>\n

Geleneksel MLP, t\u00fcm s\u0131n\u0131flar\u0131n tek bir s\u00fcper a\u011fda topland\u0131\u011f\u0131, hepsi bir arada a\u011f (ACON) yap\u0131s\u0131n\u0131 benimser. S\u00fcper a\u011f\u0131n t\u00fcm \u00f6\u011fretmenleri ayn\u0131 anda tatmin etme y\u00fck\u00fc vard\u0131r, bu nedenle gizli birimlerin say\u0131s\u0131 b\u00fcy\u00fck olma e\u011filimindedir. Ampirik sonu\u00e7lar, gizli birimlerin e\u011fitimi farkl\u0131 \u00f6\u011fretmenlerden gelen (potansiyel olarak \u00e7eli\u015fkili) sinyallerden etkilendi\u011fi i\u00e7in, ACON’un yak\u0131nsama oran\u0131n\u0131n a\u011f boyutuna g\u00f6re b\u00fcy\u00fck \u00f6l\u00e7\u00fcde d\u00fc\u015ft\u00fc\u011f\u00fcn\u00fc do\u011frulamaktad\u0131r.<\/span><\/p>\n

Buna kar\u015f\u0131l\u0131k, RBF’nin, bir alt a\u011f\u0131n yaln\u0131zca bir s\u0131n\u0131fa atand\u0131\u011f\u0131 a\u011fda bir s\u0131n\u0131f (OCON) yap\u0131s\u0131n\u0131 ba\u015fka bir a\u011f yap\u0131s\u0131 t\u00fcr\u00fcn\u00fc benimsemesi do\u011fald\u0131r. Bu iki yap\u0131 aras\u0131ndaki fark g\u00f6sterilmektedir.<\/span><\/p>\n

OCON a\u011f\u0131ndaki her alt a\u011f, kendi s\u0131n\u0131f\u0131n\u0131 di\u011ferlerinden ay\u0131rma konusunda uzmanla\u015fm\u0131\u015ft\u0131r, dolay\u0131s\u0131yla gizli birimlerin say\u0131s\u0131 genellikle azd\u0131r.<\/span><\/p>\n

Ayr\u0131ca OCON yap\u0131lar\u0131 a\u015fa\u011f\u0131daki \u00f6zelliklere sahiptir:<\/span><\/strong><\/p>\n

\u2022 Bireysel alt a\u011flar i\u00e7in ba\u015flang\u0131\u00e7 a\u011f\u0131rl\u0131klar\u0131n\u0131 belirlemek \u00fczere yerel olarak denetimsiz \u00f6\u011frenme uygulanabilir. \u0130lk k\u00fcmeler, VQ veya K-ortalama k\u00fcmeleme teknikleri ile e\u011fitilebilir. K\u00fcme olas\u0131l\u0131klar\u0131 isteniyorsa, her s\u0131n\u0131f ko\u015fullu olabilirlik yo\u011funlu\u011fu i\u00e7in maksimum olabilirlik tahminini elde etmek i\u00e7in EM algoritmas\u0131 uygulanabilir.<\/span>
\n\u2022OCON yap\u0131s\u0131, art\u0131ml\u0131 e\u011fitim i\u00e7in uygundur (yani, \u00fcyeliklerin eklenmesi\/\u00e7\u0131kar\u0131lmas\u0131 yoluyla a\u011f y\u00fckseltme [57, 58]).<\/span>
\n\u2022 OCON a\u011f yap\u0131s\u0131, da\u011f\u0131t\u0131lm\u0131\u015f i\u015fleme kavram\u0131n\u0131 destekler. Ak\u0131ll\u0131 kartl\u0131 biyometrik sistemlere hitap etmektedir. OCON tipi bir s\u0131n\u0131fland\u0131r\u0131c\u0131, ki\u015fisel ay\u0131rt edici kodlar\u0131 ayr\u0131 s\u0131n\u0131f alt a\u011flar\u0131nda depolayabilir, bu nedenle karttaki m\u0131knat\u0131s \u015feridinin, alt a\u011fda yaln\u0131zca kart sahibine atanm\u0131\u015f olan a\u011f parametrelerini saklamas\u0131 gerekir.<\/span><\/p>\n

Uygulama \u00f6rnekleri:<\/strong> Ba\u015far\u0131l\u0131 y\u00fcz tan\u0131ma uygulamalar\u0131 i\u00e7in \u201chyperBF\u201d a\u011f\u0131 olarak adland\u0131r\u0131lan \u00f6zel bir RBF a\u011f\u0131 t\u00fcr\u00fc \u00f6nermi\u015ftir. \u0130\u00e7inde, ilgili ses bilgisinden video sahnesi s\u0131n\u0131fland\u0131rmas\u0131 i\u00e7in yararlan\u0131l\u0131r. \u00c7e\u015fitli ses \u00f6zelliklerinin, farkl\u0131 sahne s\u0131n\u0131flar\u0131n\u0131n ses \u00f6zelliklerini ay\u0131rt etmede etkili oldu\u011fu bulunmu\u015ftur. Bu \u00f6zelliklere dayanarak, bir sinir a\u011f\u0131 s\u0131n\u0131fland\u0131r\u0131c\u0131s\u0131, ses kliplerini farkl\u0131 TV programlar\u0131ndan ba\u015far\u0131yla ay\u0131rabilir.<\/span><\/p>\n

Karar Tabanl\u0131 Sinir A\u011f\u0131<\/span><\/strong><\/h3>\n

Karara dayal\u0131 bir sinir a\u011f\u0131n\u0131n (DBNN) iki \u00e7e\u015fidi vard\u0131r: biri zor karar modeli ve di\u011feri olas\u0131l\u0131k modelidir. ADBNNhasamodularOCONa\u011f yap\u0131s\u0131: bir alt a\u011f, bir nesne s\u0131n\u0131f\u0131n\u0131 temsil edecek \u015fekilde atan\u0131r.<\/span><\/p>\n

\u00c7ok s\u0131n\u0131fl\u0131 s\u0131n\u0131fland\u0131rma problemlerinde, alt a\u011flar\u0131n \u00e7\u0131kt\u0131lar\u0131 (ay\u0131r\u0131c\u0131 i\u015flevler) birbiriyle rekabet edecek ve en b\u00fcy\u00fck \u00e7\u0131kt\u0131 de\u011ferine sahip alt a\u011f, girdi modelinin kimli\u011fini talep edecektir.<\/span><\/p>\n

Karar Tabanl\u0131 \u00d6\u011frenme Kural\u0131<\/span><\/strong><\/h3>\n

DBNN’nin \u00f6\u011frenme \u015femas\u0131 iki a\u015famaya ayr\u0131lm\u0131\u015ft\u0131r: yerel olarak denetimsiz ve k\u00fcresel olarak denetimli \u00f6\u011frenme. Ama\u00e7, zor tahmin problemini birka\u00e7 yerelle\u015ftirilmi\u015f alt probleme b\u00f6lerek basitle\u015ftirmek ve ard\u0131ndan ince ayar s\u00fcreci minimum kaynak gerektirecektir.<\/span><\/p>\n

\u2022 Yerel Denetimsiz \u00d6\u011frenme: VQ veya EM K\u00fcmeleme Y\u00f6ntemi<\/span><\/strong><\/p>\n

Gizli d\u00fc\u011f\u00fcmlerin say\u0131s\u0131n\u0131 tahmin etmek i\u00e7in \u00e7e\u015fitli yakla\u015f\u0131mlar kullan\u0131labilir veya ilk k\u00fcmeleme, VQ veya EM k\u00fcmeleme y\u00f6ntemlerine dayal\u0131 olarak belirlenebilir.<\/span>
\n\u2013 Zor karar DBNN’de, merkezlerin ilk konumlar\u0131n\u0131 elde etmek i\u00e7in VQ tipi k\u00fcmeleme (\u00f6rn., K-ortalama) algoritmas\u0131 uygulanabilir.<\/span>
\n\u2013 PDBNN olarak adland\u0131r\u0131lan olas\u0131l\u0131ksal DBNN i\u00e7in, her bir s\u0131n\u0131f ko\u015fullu olas\u0131l\u0131k yo\u011funlu\u011fu i\u00e7in maksimum olabilirlik tahminini elde etmek \u00fczere EM algoritmas\u0131 uygulanabilir. (Olas\u0131l\u0131k yo\u011funluklar\u0131 mevcut oldu\u011funda, sonraki olas\u0131l\u0131klar\u0131n kolayca elde edilebilece\u011fini unutmay\u0131n.)<\/span><\/p>\n

\n

Aktivasyon fonksiyonu nedir<\/span>
\nAktivasyon fonksiyonu<\/a> ne ise yarar<\/span>
\nReLU aktivasyon fonksiyonu<\/span>
\nAktivasyon fonksiyonu yapay sinir a\u011flar\u0131<\/span>
\nAktivasyon fonksiyonlar\u0131<\/span>
\nSigmoid aktivasyon fonksiyonu<\/span>
\nRelu Fonksiyonu Nedir<\/span>
\nSoftmax aktivasyon fonksiyonu<\/span><\/p>\n

\n

\u2022 K\u00fcresel Denetimli \u00d6\u011frenme<\/span><\/strong><\/p>\n

Bu ba\u015flang\u0131\u00e7 ko\u015fuluna ba\u011fl\u0131 olarak, karar s\u0131n\u0131rlar\u0131na daha fazla ince ayar yapmak i\u00e7in karara dayal\u0131 \u00f6\u011frenme kural\u0131 uygulanabilir. DBNN \u00f6\u011frenme \u015femas\u0131n\u0131n ikinci a\u015famas\u0131nda, \u00f6\u011frenme s\u00fcrecinin amac\u0131 maksimum olabilirlik tahmininden minimum s\u0131n\u0131fland\u0131rma hatas\u0131na do\u011fru de\u011fi\u015fir.<\/span><\/p>\n

S\u0131n\u0131flar aras\u0131 ortak bilgi, karar s\u0131n\u0131rlar\u0131na ince ayar yapmak i\u00e7in kullan\u0131l\u0131r (yani, k\u00fcresel olarak denetlenen \u00f6\u011frenme). Bu a\u015famada DBNN, a\u011f parametrelerini ayarlamak i\u00e7in g\u00fc\u00e7lendirilmi\u015f-anti-g\u00fc\u00e7lendirilmi\u015f \u00f6\u011frenme kural\u0131n\u0131 veya ayr\u0131mc\u0131 \u00f6\u011frenme kural\u0131n\u0131 uygular. Bu e\u011fitim a\u015famas\u0131nda yaln\u0131zca yanl\u0131\u015f s\u0131n\u0131fland\u0131r\u0131lm\u0131\u015f modeller yer al\u0131r.<\/span><\/p>\n

DBNN, OCR ve doku s\u0131n\u0131fland\u0131rmas\u0131 ile y\u00fcz ve avu\u00e7 i\u00e7i tan\u0131ma problemleri gibi bir\u00e7ok \u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma problemi i\u00e7in etkili bir sinir a\u011f\u0131d\u0131r. Etkile\u015fimli insan-bilgisayar g\u00f6r\u00fc\u015f\u00fc i\u00e7in son zamanlarda DBNN’ye dayal\u0131 mod\u00fcler bir sinir a\u011f\u0131 ve model tabanl\u0131 bir sinir a\u011f\u0131 \u00f6nerilmi\u015ftir.<\/span><\/p>\n

Uzman Kar\u0131\u015f\u0131m\u0131<\/span><\/strong><\/h3>\n

Uzman kar\u0131\u015f\u0131m\u0131 (MOE) \u00f6\u011freniminin, \u00e7\u00f6z\u00fcmleri e\u011fitilebilir ge\u00e7itleme kullan\u0131larak dinamik olarak tutarl\u0131 bir bire entegre edilen daha k\u00fc\u00e7\u00fck ve mod\u00fcler hale getirilmi\u015f e\u011fitilebilir a\u011flar (yani uzmanlar) taraf\u0131ndan b\u00fcy\u00fck ve karma\u015f\u0131k bir g\u00f6revi etkili bir \u015fekilde \u00e7\u00f6zme becerisi nedeniyle daha iyi performans sa\u011flad\u0131\u011f\u0131 g\u00f6sterilmi\u015ftir. a\u011f. Belirli bir x girdisi i\u00e7in, K uzman\u0131 kullan\u0131larak x verilen y s\u0131n\u0131f\u0131n\u0131 olu\u015fturman\u0131n sonsal olas\u0131l\u0131\u011f\u0131 hesaplan\u0131r.<\/span><\/p>\n

Burada y bir ikili vekt\u00f6rd\u00fcr, \u03c6 bir parametre vekt\u00f6r\u00fcd\u00fcr [v, \u03b8i ], gi uzman \u00e7\u0131kt\u0131lar\u0131n\u0131 a\u011f\u0131rl\u0131kland\u0131rma olas\u0131l\u0131\u011f\u0131d\u0131r, v ge\u00e7itleme a\u011f\u0131 i\u00e7in parametrelerin bir vekt\u00f6r\u00fcd\u00fcr, \u03b8i i’inci i\u00e7in parametrelerin bir vekt\u00f6r\u00fcd\u00fcr uzman a\u011f\u0131 (i = 1,…,K) ve P(y|x,\u03b8i) i. uzman a\u011f\u0131n\u0131n \u00e7\u0131kt\u0131s\u0131d\u0131r.<\/span><\/p>\n

Ge\u00e7itleme a\u011f\u0131, do\u011frusal olmayan bir sinir a\u011f\u0131 veya do\u011frusal bir sinir a\u011f\u0131 olabilir. Do\u011frusal yolluk a\u011f \u00e7\u0131k\u0131\u015f\u0131n\u0131 elde etmek i\u00e7in softmax i\u015flevi kullan\u0131l\u0131r.<\/span><\/p>\n

MOE i\u00e7in \u00f6\u011frenme algoritmas\u0131, parametreleri tahmin etmek i\u00e7in maksimum olabilirlik ilkesine dayanmaktad\u0131r (yani, verilen e\u011fitim setinin olas\u0131l\u0131\u011f\u0131n\u0131n en y\u00fcksek oldu\u011fu parametreleri se\u00e7in). Gradyan y\u00fckselme algoritmas\u0131, parametreleri tahmin etmek i\u00e7in kullan\u0131labilir.<\/span><\/p>\n

MOE, her biri girdi uzay\u0131n\u0131n belirli bir b\u00f6l\u00fcm\u00fcnde s\u0131n\u0131fland\u0131rma g\u00f6revlerini yerine getiren bir dizi “uzman\u0131n” \u00e7\u0131kt\u0131lar\u0131n\u0131n, her bir b\u00f6l\u00fcm\u00fcn olas\u0131l\u0131\u011f\u0131n\u0131 modelleyen bir “ge\u00e7itleme” a\u011f\u0131 taraf\u0131ndan olas\u0131l\u0131ksal bir \u015fekilde birle\u015ftirildi\u011fi mod\u00fcler bir mimaridir. Girdi alan\u0131n\u0131n y\u00fczdesi nihai a\u011f \u00e7\u0131kt\u0131s\u0131n\u0131 olu\u015fturur.<\/span><\/p>\n

Her bir yerel uzman a\u011f\u0131, her modelin yaln\u0131zca bir s\u0131n\u0131f\u0131 temsil etti\u011fi K’dan ba\u011f\u0131ms\u0131z bir binom modeli veya t\u00fcm s\u0131n\u0131flar i\u00e7in bir \u00e7ok terimli model kullanarak K s\u0131n\u0131flar\u0131 \u00fczerinde \u00e7ok yollu s\u0131n\u0131fland\u0131rma ger\u00e7ekle\u015ftirir.<\/span><\/p>\n

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