{"id":477450,"date":"2023-08-09T09:15:09","date_gmt":"2023-08-09T09:15:09","guid":{"rendered":""},"modified":"2023-09-05T11:14:43","modified_gmt":"2023-09-05T11:14:43","slug":"hidden-markov-models","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/hidden-markov-models\/","title":{"rendered":"M\u00f4 h\u00ecnh Markov \u1ea9n"},"content":{"rendered":"

M\u00f4 h\u00ecnh Markov \u1ea9n (HMM) l\u00e0 m\u00f4 h\u00ecnh th\u1ed1ng k\u00ea \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 th\u1ec3 hi\u1ec7n c\u00e1c h\u1ec7 th\u1ed1ng ph\u00e1t tri\u1ec3n theo th\u1eddi gian. Ch\u00fang th\u01b0\u1eddng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng trong c\u00e1c l\u0129nh v\u1ef1c nh\u01b0 h\u1ecdc m\u00e1y, nh\u1eadn d\u1ea1ng m\u1eabu v\u00e0 sinh h\u1ecdc t\u00ednh to\u00e1n, nh\u1edd kh\u1ea3 n\u0103ng m\u00f4 h\u00ecnh h\u00f3a c\u00e1c qu\u00e1 tr\u00ecnh ng\u1eabu nhi\u00ean ph\u1ee9c t\u1ea1p, ph\u1ee5 thu\u1ed9c v\u00e0o th\u1eddi gian.<\/p>\n

Truy t\u00ecm s\u1ef1 kh\u1edfi \u0111\u1ea7u: Ngu\u1ed3n g\u1ed1c v\u00e0 s\u1ef1 ph\u00e1t tri\u1ec3n c\u1ee7a c\u00e1c m\u00f4 h\u00ecnh Markov \u1ea9n<\/h2>\n

Khung l\u00fd thuy\u1ebft c\u1ee7a M\u00f4 h\u00ecnh Markov \u1ea9n l\u1ea7n \u0111\u1ea7u ti\u00ean \u0111\u01b0\u1ee3c \u0111\u1ec1 xu\u1ea5t v\u00e0o cu\u1ed1i nh\u1eefng n\u0103m 1960 b\u1edfi Leonard E. Baum v\u00e0 c\u00e1c \u0111\u1ed3ng nghi\u1ec7p c\u1ee7a \u00f4ng. Ban \u0111\u1ea7u, ch\u00fang \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng trong c\u00f4ng ngh\u1ec7 nh\u1eadn d\u1ea1ng gi\u1ecdng n\u00f3i v\u00e0 tr\u1edf n\u00ean ph\u1ed5 bi\u1ebfn v\u00e0o nh\u1eefng n\u0103m 1970 khi \u0111\u01b0\u1ee3c IBM s\u1eed d\u1ee5ng trong h\u1ec7 th\u1ed1ng nh\u1eadn d\u1ea1ng gi\u1ecdng n\u00f3i \u0111\u1ea7u ti\u00ean c\u1ee7a h\u1ecd. Nh\u1eefng m\u00f4 h\u00ecnh n\u00e0y \u0111\u00e3 \u0111\u01b0\u1ee3c \u0111i\u1ec1u ch\u1ec9nh v\u00e0 n\u00e2ng cao k\u1ec3 t\u1eeb \u0111\u00f3, g\u00f3p ph\u1ea7n \u0111\u00e1ng k\u1ec3 v\u00e0o s\u1ef1 ph\u00e1t tri\u1ec3n c\u1ee7a tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o v\u00e0 h\u1ecdc m\u00e1y.<\/p>\n

C\u00e1c m\u00f4 h\u00ecnh Markov \u1ea9n: H\u00e9 l\u1ed9 nh\u1eefng chi\u1ec1u s\u00e2u ti\u1ec1m \u1ea9n<\/h2>\n

HMM \u0111\u1eb7c bi\u1ec7t ph\u00f9 h\u1ee3p v\u1edbi c\u00e1c v\u1ea5n \u0111\u1ec1 li\u00ean quan \u0111\u1ebfn d\u1ef1 \u0111o\u00e1n, l\u1ecdc, l\u00e0m m\u1ecbn v\u00e0 t\u00ecm l\u1eddi gi\u1ea3i th\u00edch cho m\u1ed9t t\u1eadp h\u1ee3p c\u00e1c bi\u1ebfn \u0111\u01b0\u1ee3c quan s\u00e1t d\u1ef1a tr\u00ean \u0111\u1ed9ng l\u1ef1c c\u1ee7a m\u1ed9t t\u1eadp h\u1ee3p bi\u1ebfn kh\u00f4ng \u0111\u01b0\u1ee3c quan s\u00e1t ho\u1eb7c \u201c\u1ea9n\u201d. Ch\u00fang l\u00e0 tr\u01b0\u1eddng h\u1ee3p \u0111\u1eb7c bi\u1ec7t c\u1ee7a c\u00e1c m\u00f4 h\u00ecnh Markov, trong \u0111\u00f3 h\u1ec7 th\u1ed1ng \u0111ang \u0111\u01b0\u1ee3c m\u00f4 h\u00ecnh h\u00f3a \u0111\u01b0\u1ee3c gi\u1ea3 \u0111\u1ecbnh l\u00e0 m\u1ed9t quy tr\u00ecnh Markov - ngh\u0129a l\u00e0 m\u1ed9t quy tr\u00ecnh ng\u1eabu nhi\u00ean kh\u00f4ng c\u00f3 b\u1ed9 nh\u1edb - v\u1edbi c\u00e1c tr\u1ea1ng th\u00e1i (\u201c\u1ea9n\u201d) kh\u00f4ng th\u1ec3 quan s\u00e1t \u0111\u01b0\u1ee3c.<\/p>\n

V\u1ec1 b\u1ea3n ch\u1ea5t, HMM cho ph\u00e9p ch\u00fang ta n\u00f3i v\u1ec1 c\u1ea3 c\u00e1c s\u1ef1 ki\u1ec7n \u0111\u01b0\u1ee3c quan s\u00e1t (nh\u01b0 c\u00e1c t\u1eeb m\u00e0 ch\u00fang ta nh\u00ecn th\u1ea5y trong \u0111\u1ea7u v\u00e0o) v\u00e0 c\u00e1c s\u1ef1 ki\u1ec7n \u1ea9n (nh\u01b0 c\u1ea5u tr\u00fac ng\u1eef ph\u00e1p) m\u00e0 ch\u00fang ta coi l\u00e0 y\u1ebfu t\u1ed1 nh\u00e2n qu\u1ea3 trong c\u00e1c s\u1ef1 ki\u1ec7n \u0111\u01b0\u1ee3c quan s\u00e1t.<\/p>\n

Ho\u1ea1t \u0111\u1ed9ng b\u00ean trong: C\u00e1c m\u00f4 h\u00ecnh Markov \u1ea9n ho\u1ea1t \u0111\u1ed9ng nh\u01b0 th\u1ebf n\u00e0o<\/h2>\n

C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a HMM bao g\u1ed3m hai ph\u1ea7n c\u01a1 b\u1ea3n:<\/p>\n

    \n
  1. Chu\u1ed7i c\u00e1c bi\u1ebfn quan s\u00e1t \u0111\u01b0\u1ee3c<\/li>\n
  2. Chu\u1ed7i c\u00e1c bi\u1ebfn \u1ea9n<\/li>\n<\/ol>\n

    M\u00f4 h\u00ecnh Markov \u1ea9n bao g\u1ed3m quy tr\u00ecnh Markov, trong \u0111\u00f3 tr\u1ea1ng th\u00e1i kh\u00f4ng hi\u1ec3n th\u1ecb tr\u1ef1c ti\u1ebfp nh\u01b0ng \u0111\u1ea7u ra, ph\u1ee5 thu\u1ed9c v\u00e0o tr\u1ea1ng th\u00e1i, hi\u1ec3n th\u1ecb. M\u1ed7i tr\u1ea1ng th\u00e1i c\u00f3 ph\u00e2n ph\u1ed1i x\u00e1c su\u1ea5t tr\u00ean c\u00e1c m\u00e3 th\u00f4ng b\u00e1o \u0111\u1ea7u ra c\u00f3 th\u1ec3 c\u00f3. V\u00ec v\u1eady, chu\u1ed7i m\u00e3 th\u00f4ng b\u00e1o do HMM t\u1ea1o ra cung c\u1ea5p m\u1ed9t s\u1ed1 th\u00f4ng tin v\u1ec1 chu\u1ed7i tr\u1ea1ng th\u00e1i, khi\u1ebfn n\u00f3 tr\u1edf th\u00e0nh m\u1ed9t quy tr\u00ecnh ng\u1eabu nhi\u00ean \u0111\u01b0\u1ee3c nh\u00fang k\u00e9p.<\/p>\n

    C\u00e1c \u0111\u1eb7c \u0111i\u1ec3m ch\u00ednh c\u1ee7a M\u00f4 h\u00ecnh Markov \u1ea9n<\/h2>\n

    C\u00e1c \u0111\u1eb7c \u0111i\u1ec3m c\u01a1 b\u1ea3n c\u1ee7a M\u00f4 h\u00ecnh Markov \u1ea9n l\u00e0:<\/p>\n

      \n
    1. Kh\u1ea3 n\u0103ng quan s\u00e1t: C\u00e1c tr\u1ea1ng th\u00e1i c\u1ee7a h\u1ec7 th\u1ed1ng kh\u00f4ng th\u1ec3 quan s\u00e1t \u0111\u01b0\u1ee3c tr\u1ef1c ti\u1ebfp.<\/li>\n
    2. T\u00ednh ch\u1ea5t Markov: M\u1ed7i tr\u1ea1ng th\u00e1i ch\u1ec9 ph\u1ee5 thu\u1ed9c v\u00e0o l\u1ecbch s\u1eed h\u1eefu h\u1ea1n c\u1ee7a c\u00e1c tr\u1ea1ng th\u00e1i tr\u01b0\u1edbc \u0111\u00f3.<\/li>\n
    3. S\u1ef1 ph\u1ee5 thu\u1ed9c th\u1eddi gian: X\u00e1c su\u1ea5t c\u00f3 th\u1ec3 thay \u0111\u1ed5i theo th\u1eddi gian.<\/li>\n
    4. Kh\u1ea3 n\u0103ng s\u00e1ng t\u1ea1o: HMM c\u00f3 th\u1ec3 t\u1ea1o ra c\u00e1c chu\u1ed7i m\u1edbi.<\/li>\n<\/ol>\n

      Ph\u00e2n lo\u1ea1i c\u00e1c m\u00f4 h\u00ecnh Markov \u1ea9n: T\u1ed5ng quan d\u1ea1ng b\u1ea3ng<\/h2>\n

      C\u00f3 ba lo\u1ea1i M\u00f4 h\u00ecnh Markov \u1ea9n ch\u00ednh, \u0111\u01b0\u1ee3c ph\u00e2n bi\u1ec7t b\u1eb1ng lo\u1ea1i ph\u00e2n b\u1ed1 x\u00e1c su\u1ea5t chuy\u1ec3n tr\u1ea1ng th\u00e1i m\u00e0 ch\u00fang s\u1eed d\u1ee5ng:<\/p>\n\n\n\n\n\n\n\n
      Ki\u1ec3u<\/th>\nS\u1ef1 mi\u00eau t\u1ea3<\/th>\n<\/tr>\n<\/thead>\n
      C\u00f4ng th\u00e1i h\u1ecdc<\/td>\nT\u1ea5t c\u1ea3 c\u00e1c ti\u1ec3u bang \u0111\u1ec1u c\u00f3 th\u1ec3 truy c\u1eadp \u0111\u01b0\u1ee3c t\u1eeb b\u1ea5t k\u1ef3 ti\u1ec3u bang n\u00e0o.<\/td>\n<\/tr>\n
      Tr\u00e1i ph\u1ea3i<\/td>\nC\u00e1c chuy\u1ec3n \u0111\u1ed5i c\u1ee5 th\u1ec3 \u0111\u01b0\u1ee3c cho ph\u00e9p, th\u01b0\u1eddng l\u00e0 theo h\u01b0\u1edbng chuy\u1ec3n ti\u1ebfp.<\/td>\n<\/tr>\n
      \u0110\u00e3 k\u1ebft n\u1ed1i \u0111\u1ea7y \u0111\u1ee7<\/td>\nB\u1ea5t k\u1ef3 tr\u1ea1ng th\u00e1i n\u00e0o c\u0169ng c\u00f3 th\u1ec3 \u0111\u1ea1t \u0111\u01b0\u1ee3c t\u1eeb b\u1ea5t k\u1ef3 tr\u1ea1ng th\u00e1i n\u00e0o kh\u00e1c trong m\u1ed9t b\u01b0\u1edbc th\u1eddi gian.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      Vi\u1ec7c s\u1eed d\u1ee5ng, th\u00e1ch th\u1ee9c v\u00e0 gi\u1ea3i ph\u00e1p li\u00ean quan \u0111\u1ebfn m\u00f4 h\u00ecnh Markov \u1ea9n<\/h2>\n

      M\u00f4 h\u00ecnh Markov \u1ea9n \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng trong nhi\u1ec1u \u1ee9ng d\u1ee5ng, bao g\u1ed3m nh\u1eadn d\u1ea1ng gi\u1ecdng n\u00f3i, tin sinh h\u1ecdc v\u00e0 d\u1ef1 \u0111o\u00e1n th\u1eddi ti\u1ebft. Tuy nhi\u00ean, ch\u00fang c\u0169ng \u0111i k\u00e8m v\u1edbi nh\u1eefng th\u00e1ch th\u1ee9c nh\u01b0 chi ph\u00ed t\u00ednh to\u00e1n cao, kh\u00f3 di\u1ec5n gi\u1ea3i c\u00e1c tr\u1ea1ng th\u00e1i \u1ea9n v\u00e0 c\u00e1c v\u1ea5n \u0111\u1ec1 trong vi\u1ec7c l\u1ef1a ch\u1ecdn m\u00f4 h\u00ecnh.<\/p>\n

      M\u1ed9t s\u1ed1 gi\u1ea3i ph\u00e1p \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 gi\u1ea3m thi\u1ec3u nh\u1eefng th\u00e1ch th\u1ee9c n\u00e0y. V\u00ed d\u1ee5: thu\u1eadt to\u00e1n Baum-Welch v\u00e0 thu\u1eadt to\u00e1n Viterbi gi\u00fap gi\u1ea3i quy\u1ebft hi\u1ec7u qu\u1ea3 b\u00e0i to\u00e1n h\u1ecdc v\u00e0 suy lu\u1eadn trong HMM.<\/p>\n

      So s\u00e1nh v\u00e0 c\u00e1c t\u00ednh n\u0103ng \u0111\u1eb7c tr\u01b0ng: HMM v\u00e0 c\u00e1c m\u00f4 h\u00ecnh t\u01b0\u01a1ng t\u1ef1<\/h2>\n

      So v\u1edbi c\u00e1c m\u00f4 h\u00ecnh t\u01b0\u01a1ng t\u1ef1 nh\u01b0 M\u1ea1ng Bayesian \u0111\u1ed9ng (DBN) v\u00e0 M\u1ea1ng th\u1ea7n kinh t\u00e1i ph\u00e1t (RNN), HMM c\u00f3 nh\u1eefng \u01b0u \u0111i\u1ec3m v\u00e0 h\u1ea1n ch\u1ebf c\u1ee5 th\u1ec3.<\/p>\n\n\n\n\n\n\n\n
      Ng\u01b0\u1eddi m\u1eabu<\/th>\nThu\u1eadn l\u1ee3i<\/th>\nH\u1ea1n ch\u1ebf<\/th>\n<\/tr>\n<\/thead>\n
      M\u00f4 h\u00ecnh Markov \u1ea9n<\/td>\nGi\u1ecfi l\u1eadp m\u00f4 h\u00ecnh d\u1eef li\u1ec7u chu\u1ed7i th\u1eddi gian, D\u1ec5 hi\u1ec3u v\u00e0 th\u1ef1c hi\u1ec7n<\/td>\nGi\u1ea3 \u0111\u1ecbnh thu\u1ed9c t\u00ednh Markov c\u00f3 th\u1ec3 qu\u00e1 h\u1ea1n ch\u1ebf \u0111\u1ed1i v\u1edbi m\u1ed9t s\u1ed1 \u1ee9ng d\u1ee5ng<\/td>\n<\/tr>\n
      M\u1ea1ng Bayesian \u0111\u1ed9ng<\/td>\nLinh ho\u1ea1t h\u01a1n HMM, C\u00f3 th\u1ec3 m\u00f4 h\u00ecnh h\u00f3a c\u00e1c ph\u1ee5 thu\u1ed9c th\u1eddi gian ph\u1ee9c t\u1ea1p<\/td>\nKh\u00f3 h\u1ecdc v\u00e0 th\u1ef1c hi\u1ec7n h\u01a1n<\/td>\n<\/tr>\n
      M\u1ea1ng th\u1ea7n kinh t\u00e1i ph\u00e1t<\/td>\nC\u00f3 th\u1ec3 x\u1eed l\u00fd c\u00e1c chu\u1ed7i d\u00e0i, C\u00f3 th\u1ec3 m\u00f4 h\u00ecnh h\u00f3a c\u00e1c h\u00e0m ph\u1ee9c t\u1ea1p<\/td>\n\u0110\u00f2i h\u1ecfi l\u01b0\u1ee3ng d\u1eef li\u1ec7u l\u1edbn, vi\u1ec7c \u0111\u00e0o t\u1ea1o c\u00f3 th\u1ec3 g\u1eb7p nhi\u1ec1u kh\u00f3 kh\u0103n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      Ch\u00e2n tr\u1eddi t\u01b0\u01a1ng lai: C\u00e1c m\u00f4 h\u00ecnh Markov \u1ea9n v\u00e0 c\u00e1c c\u00f4ng ngh\u1ec7 m\u1edbi n\u1ed5i<\/h2>\n

      Nh\u1eefng ti\u1ebfn b\u1ed9 trong t\u01b0\u01a1ng lai c\u1ee7a M\u00f4 h\u00ecnh Markov \u1ea9n c\u00f3 th\u1ec3 bao g\u1ed3m c\u00e1c ph\u01b0\u01a1ng ph\u00e1p di\u1ec5n gi\u1ea3i tr\u1ea1ng th\u00e1i \u1ea9n t\u1ed1t h\u01a1n, c\u1ea3i thi\u1ec7n hi\u1ec7u qu\u1ea3 t\u00ednh to\u00e1n v\u00e0 m\u1edf r\u1ed9ng sang c\u00e1c l\u0129nh v\u1ef1c \u1ee9ng d\u1ee5ng m\u1edbi nh\u01b0 \u0111i\u1ec7n to\u00e1n l\u01b0\u1ee3ng t\u1eed v\u00e0 thu\u1eadt to\u00e1n AI ti\u00ean ti\u1ebfn.<\/p>\n

      M\u00e1y ch\u1ee7 proxy v\u00e0 m\u00f4 h\u00ecnh Markov \u1ea9n: M\u1ed9t li\u00ean minh \u0111\u1ed9c \u0111\u00e1o<\/h2>\n

      M\u00f4 h\u00ecnh Markov \u1ea9n c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 ph\u00e2n t\u00edch v\u00e0 d\u1ef1 \u0111o\u00e1n c\u00e1c m\u1eabu l\u01b0u l\u01b0\u1ee3ng truy c\u1eadp m\u1ea1ng, m\u1ed9t kh\u1ea3 n\u0103ng c\u00f3 gi\u00e1 tr\u1ecb cho c\u00e1c m\u00e1y ch\u1ee7 proxy. M\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 s\u1eed d\u1ee5ng HMM \u0111\u1ec3 ph\u00e2n lo\u1ea1i l\u01b0u l\u01b0\u1ee3ng truy c\u1eadp v\u00e0 ph\u00e1t hi\u1ec7n c\u00e1c \u0111i\u1ec3m b\u1ea5t th\u01b0\u1eddng, c\u1ea3i thi\u1ec7n t\u00ednh b\u1ea3o m\u1eadt v\u00e0 hi\u1ec7u qu\u1ea3.<\/p>\n

      Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n

      \u0110\u1ec3 bi\u1ebft th\u00eam th\u00f4ng tin v\u1ec1 M\u00f4 h\u00ecnh Markov \u1ea9n, h\u00e3y xem x\u00e9t truy c\u1eadp c\u00e1c t\u00e0i nguy\u00ean sau:<\/p>\n

        \n
      1. M\u00f4 h\u00ecnh Markov \u1ea9n (\u0110\u1ea1i h\u1ecdc Stanford)<\/a><\/li>\n
      2. H\u01b0\u1edbng d\u1eabn v\u1ec1 M\u00f4 h\u00ecnh Markov \u1ea9n (\u0110\u1ea1i h\u1ecdc Leeds)<\/a><\/li>\n
      3. Gi\u1edbi thi\u1ec7u v\u1ec1 M\u00f4 h\u00ecnh Markov \u1ea9n (MIT)<\/a><\/li>\n
      4. H\u1ecdc t\u1eadp trong c\u00e1c m\u00f4 h\u00ecnh Markov \u1ea9n (T\u1ef1 nhi\u00ean)<\/a><\/li>\n<\/ol>","protected":false},"featured_media":468545,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477450","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about Hidden Markov Models: Unraveling the Invisible Patterns<\/mark>","faq_items":[{"question":"What is a Hidden Markov Model?","answer":"

        A Hidden Markov Model is a statistical model that is used to represent systems that evolve over time. They are well-suited to problems involving prediction, filtering, smoothing, and finding explanations for a set of observed variables based on the dynamics of an unobserved or \"hidden\" set of variables.<\/p>"},{"question":"Who first proposed the concept of Hidden Markov Models?","answer":"

        The theoretical framework of Hidden Markov Models was first proposed in the late 1960s by Leonard E. Baum and his colleagues.<\/p>"},{"question":"What are the key features of Hidden Markov Models?","answer":"

        The essential features of Hidden Markov Models include observability, the Markov property, time dependence, and generativity. The system's states are not directly observable, each state depends only on a finite history of previous states, the probabilities can change over time, and HMMs can generate new sequences.<\/p>"},{"question":"What are the types of Hidden Markov Models?","answer":"

        There are three primary types of Hidden Markov Models: Ergodic, in which all states are reachable from any state; Left-right, where specific transitions are allowed, typically in a forward direction; and Fully connected, where any state can be reached from any other state in one time step.<\/p>"},{"question":"What are the common applications of Hidden Markov Models?","answer":"

        Hidden Markov Models are used in a variety of applications, including speech recognition, bioinformatics, and weather prediction.<\/p>"},{"question":"What challenges are associated with the use of Hidden Markov Models?","answer":"

        Challenges associated with Hidden Markov Models include high computational cost, difficulty in interpreting hidden states, and issues with model selection.<\/p>"},{"question":"How are Hidden Markov Models related to proxy servers?","answer":"

        Hidden Markov Models can be used to analyze and predict network traffic patterns, which is valuable for proxy servers. Proxy servers can utilize HMMs to classify traffic and detect anomalies, thus improving security and efficiency.<\/p>"},{"question":"What is the future perspective of Hidden Markov Models?","answer":"

        Future advancements in Hidden Markov Models may include methods to better interpret hidden states, improvements in computation efficiency, and expansion into new areas of application like quantum computing and advanced AI algorithms.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/477450","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/477450\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/468545"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=477450"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}