{"id":478919,"date":"2023-08-09T09:40:22","date_gmt":"2023-08-09T09:40:22","guid":{"rendered":""},"modified":"2023-09-05T11:17:48","modified_gmt":"2023-09-05T11:17:48","slug":"semi-supervised-learning","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/semi-supervised-learning\/","title":{"rendered":"H\u1ecdc b\u00e1n gi\u00e1m s\u00e1t"},"content":{"rendered":"<p>H\u1ecdc b\u00e1n gi\u00e1m s\u00e1t l\u00e0 m\u1ed9t m\u00f4 h\u00ecnh h\u1ecdc m\u00e1y s\u1eed d\u1ee5ng c\u1ea3 d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c d\u00e1n nh\u00e3n v\u00e0 kh\u00f4ng \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n trong qu\u00e1 tr\u00ecnh \u0111\u00e0o t\u1ea1o. N\u00f3 thu h\u1eb9p kho\u1ea3ng c\u00e1ch gi\u1eefa h\u1ecdc c\u00f3 gi\u00e1m s\u00e1t, ho\u00e0n to\u00e0n d\u1ef1a v\u00e0o d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c d\u00e1n nh\u00e3n v\u00e0 h\u1ecdc kh\u00f4ng gi\u00e1m s\u00e1t, ho\u1ea1t \u0111\u1ed9ng m\u00e0 kh\u00f4ng c\u00f3 d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c d\u00e1n nh\u00e3n n\u00e0o c\u1ea3. C\u00e1ch ti\u1ebfp c\u1eadn n\u00e0y cho ph\u00e9p m\u00f4 h\u00ecnh t\u1eadn d\u1ee5ng m\u1ed9t l\u01b0\u1ee3ng l\u1edbn d\u1eef li\u1ec7u ch\u01b0a \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n, c\u00f9ng v\u1edbi m\u1ed9t t\u1eadp h\u1ee3p d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n nh\u1ecf h\u01a1n, \u0111\u1ec3 \u0111\u1ea1t \u0111\u01b0\u1ee3c hi\u1ec7u su\u1ea5t t\u1ed1t h\u01a1n.<\/p>\n<h2>L\u1ecbch s\u1eed ngu\u1ed3n g\u1ed1c c\u1ee7a h\u1ecdc t\u1eadp b\u00e1n gi\u00e1m s\u00e1t v\u00e0 s\u1ef1 \u0111\u1ec1 c\u1eadp \u0111\u1ea7u ti\u00ean v\u1ec1 n\u00f3<\/h2>\n<p>H\u1ecdc b\u00e1n gi\u00e1m s\u00e1t c\u00f3 ngu\u1ed3n g\u1ed1c t\u1eeb c\u00e1c nghi\u00ean c\u1ee9u nh\u1eadn d\u1ea1ng m\u1eabu c\u1ee7a th\u1ebf k\u1ef7 20. \u00dd t\u01b0\u1edfng n\u00e0y l\u1ea7n \u0111\u1ea7u ti\u00ean \u0111\u01b0\u1ee3c c\u00e1c nh\u00e0 nghi\u00ean c\u1ee9u g\u1ee3i \u00fd v\u00e0o nh\u1eefng n\u0103m 1960, h\u1ecd nh\u1eadn ra r\u1eb1ng vi\u1ec7c s\u1eed d\u1ee5ng c\u1ea3 d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c d\u00e1n nh\u00e3n v\u00e0 kh\u00f4ng \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n c\u00f3 th\u1ec3 c\u1ea3i thi\u1ec7n hi\u1ec7u qu\u1ea3 c\u1ee7a m\u00f4 h\u00ecnh. B\u1ea3n th\u00e2n thu\u1eadt ng\u1eef n\u00e0y \u0111\u01b0\u1ee3c thi\u1ebft l\u1eadp ch\u00ednh th\u1ee9c h\u01a1n v\u00e0o cu\u1ed1i nh\u1eefng n\u0103m 1990, v\u1edbi s\u1ef1 \u0111\u00f3ng g\u00f3p \u0111\u00e1ng k\u1ec3 t\u1eeb c\u00e1c nh\u00e0 nghi\u00ean c\u1ee9u nh\u01b0 Yoshua Bengio v\u00e0 c\u00e1c nh\u00e2n v\u1eadt h\u00e0ng \u0111\u1ea7u kh\u00e1c trong l\u0129nh v\u1ef1c n\u00e0y.<\/p>\n<h2>Th\u00f4ng tin chi ti\u1ebft v\u1ec1 h\u1ecdc t\u1eadp b\u00e1n gi\u00e1m s\u00e1t: M\u1edf r\u1ed9ng ch\u1ee7 \u0111\u1ec1<\/h2>\n<p>H\u1ecdc b\u00e1n gi\u00e1m s\u00e1t s\u1eed d\u1ee5ng k\u1ebft h\u1ee3p d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c d\u00e1n nh\u00e3n (m\u1ed9t t\u1eadp h\u1ee3p nh\u1ecf c\u00e1c v\u00ed d\u1ee5 v\u1edbi k\u1ebft qu\u1ea3 \u0111\u00e3 bi\u1ebft) v\u00e0 d\u1eef li\u1ec7u kh\u00f4ng \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n (m\u1ed9t t\u1eadp h\u1ee3p l\u1edbn c\u00e1c v\u00ed d\u1ee5 kh\u00f4ng c\u00f3 k\u1ebft qu\u1ea3 \u0111\u00e3 bi\u1ebft). N\u00f3 gi\u1ea3 \u0111\u1ecbnh r\u1eb1ng c\u1ea5u tr\u00fac c\u01a1 b\u1ea3n c\u1ee7a d\u1eef li\u1ec7u c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c n\u1eafm b\u1eaft b\u1eb1ng c\u00e1ch s\u1eed d\u1ee5ng c\u1ea3 hai lo\u1ea1i d\u1eef li\u1ec7u, cho ph\u00e9p m\u00f4 h\u00ecnh kh\u00e1i qu\u00e1t h\u00f3a t\u1ed1t h\u01a1n t\u1eeb m\u1ed9t t\u1eadp h\u1ee3p nh\u1ecf h\u01a1n c\u00e1c v\u00ed d\u1ee5 \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n.<\/p>\n<h3>Ph\u01b0\u01a1ng ph\u00e1p h\u1ecdc b\u00e1n gi\u00e1m s\u00e1t<\/h3>\n<ol>\n<li><strong>T\u1ef1 \u0111\u00e0o t\u1ea1o<\/strong>: D\u1eef li\u1ec7u ch\u01b0a \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n s\u1ebd \u0111\u01b0\u1ee3c ph\u00e2n lo\u1ea1i v\u00e0 sau \u0111\u00f3 \u0111\u01b0\u1ee3c th\u00eam v\u00e0o t\u1eadp hu\u1ea5n luy\u1ec7n.<\/li>\n<li><strong>\u0110\u00e0o t\u1ea1o nhi\u1ec1u ch\u1ebf \u0111\u1ed9 xem<\/strong>: C\u00e1c ch\u1ebf \u0111\u1ed9 xem d\u1eef li\u1ec7u kh\u00e1c nhau \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 t\u00ecm hi\u1ec3u nhi\u1ec1u b\u1ed9 ph\u00e2n lo\u1ea1i.<\/li>\n<li><strong>\u0110\u1ed3ng \u0111\u00e0o t\u1ea1o<\/strong>: Nhi\u1ec1u b\u1ed9 ph\u00e2n lo\u1ea1i \u0111\u01b0\u1ee3c hu\u1ea5n luy\u1ec7n tr\u00ean c\u00e1c t\u1eadp h\u1ee3p con d\u1eef li\u1ec7u ng\u1eabu nhi\u00ean kh\u00e1c nhau v\u00e0 sau \u0111\u00f3 \u0111\u01b0\u1ee3c k\u1ebft h\u1ee3p l\u1ea1i.<\/li>\n<li><strong>Ph\u01b0\u01a1ng ph\u00e1p d\u1ef1a tr\u00ean \u0111\u1ed3 th\u1ecb<\/strong>: C\u1ea5u tr\u00fac c\u1ee7a d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c bi\u1ec3u di\u1ec5n d\u01b0\u1edbi d\u1ea1ng bi\u1ec3u \u0111\u1ed3 \u0111\u1ec3 x\u00e1c \u0111\u1ecbnh m\u1ed1i quan h\u1ec7 gi\u1eefa c\u00e1c phi\u00ean b\u1ea3n \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n v\u00e0 kh\u00f4ng \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n.<\/li>\n<\/ol>\n<h2>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a vi\u1ec7c h\u1ecdc b\u00e1n gi\u00e1m s\u00e1t: C\u00e1ch th\u1ee9c ho\u1ea1t \u0111\u1ed9ng<\/h2>\n<p>C\u00e1c thu\u1eadt to\u00e1n h\u1ecdc b\u00e1n gi\u00e1m s\u00e1t ho\u1ea1t \u0111\u1ed9ng b\u1eb1ng c\u00e1ch t\u00ecm c\u00e1c c\u1ea5u tr\u00fac \u1ea9n trong d\u1eef li\u1ec7u ch\u01b0a \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n c\u00f3 th\u1ec3 n\u00e2ng cao vi\u1ec7c h\u1ecdc t\u1eeb d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n. Qu\u00e1 tr\u00ecnh n\u00e0y th\u01b0\u1eddng bao g\u1ed3m c\u00e1c b\u01b0\u1edbc sau:<\/p>\n<ol>\n<li><strong>Kh\u1edfi t\u1ea1o<\/strong>: B\u1eaft \u0111\u1ea7u v\u1edbi t\u1eadp d\u1eef li\u1ec7u c\u00f3 nh\u00e3n nh\u1ecf v\u00e0 t\u1eadp d\u1eef li\u1ec7u l\u1edbn kh\u00f4ng c\u00f3 nh\u00e3n.<\/li>\n<li><strong>\u0110\u00e0o t\u1ea1o ng\u01b0\u1eddi m\u1eabu<\/strong>: Hu\u1ea5n luy\u1ec7n ban \u0111\u1ea7u v\u1ec1 d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c d\u00e1n nh\u00e3n.<\/li>\n<li><strong>S\u1eed d\u1ee5ng d\u1eef li\u1ec7u kh\u00f4ng \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n<\/strong>: S\u1eed d\u1ee5ng m\u00f4 h\u00ecnh \u0111\u1ec3 d\u1ef1 \u0111o\u00e1n k\u1ebft qu\u1ea3 cho d\u1eef li\u1ec7u kh\u00f4ng \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n.<\/li>\n<li><strong>Tinh ch\u1ec9nh l\u1eb7p \u0111i l\u1eb7p l\u1ea1i<\/strong>: Tinh ch\u1ec9nh m\u00f4 h\u00ecnh b\u1eb1ng c\u00e1ch th\u00eam c\u00e1c d\u1ef1 \u0111o\u00e1n \u0111\u00e1ng tin c\u1eady d\u01b0\u1edbi d\u1ea1ng d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n m\u1edbi.<\/li>\n<li><strong>\u0110\u00e0o t\u1ea1o m\u1eabu cu\u1ed1i c\u00f9ng<\/strong>: Hu\u1ea5n luy\u1ec7n m\u00f4 h\u00ecnh tinh t\u1ebf \u0111\u1ec3 d\u1ef1 \u0111o\u00e1n ch\u00ednh x\u00e1c h\u01a1n.<\/li>\n<\/ol>\n<h2>Ph\u00e2n t\u00edch c\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a h\u1ecdc t\u1eadp b\u00e1n gi\u00e1m s\u00e1t<\/h2>\n<ul>\n<li><strong>Hi\u1ec7u qu\u1ea3<\/strong>: S\u1eed d\u1ee5ng m\u1ed9t l\u01b0\u1ee3ng l\u1edbn d\u1eef li\u1ec7u ch\u01b0a \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n c\u00f3 s\u1eb5n.<\/li>\n<li><strong>Hi\u1ec7u qu\u1ea3 v\u1ec1 chi ph\u00ed<\/strong>: Gi\u1ea3m nhu c\u1ea7u n\u1ed7 l\u1ef1c ghi nh\u00e3n t\u1ed1n k\u00e9m.<\/li>\n<li><strong>Uy\u1ec3n chuy\u1ec3n<\/strong>: \u00c1p d\u1ee5ng tr\u00ean nhi\u1ec1u l\u0129nh v\u1ef1c v\u00e0 nhi\u1ec7m v\u1ee5 kh\u00e1c nhau.<\/li>\n<li><strong>Th\u1eed th\u00e1ch<\/strong>: Vi\u1ec7c x\u1eed l\u00fd d\u1eef li\u1ec7u nhi\u1ec5u v\u00e0 ghi nh\u00e3n kh\u00f4ng ch\u00ednh x\u00e1c c\u00f3 th\u1ec3 ph\u1ee9c t\u1ea1p.<\/li>\n<\/ul>\n<h2>C\u00e1c lo\u1ea1i h\u00ecnh h\u1ecdc t\u1eadp b\u00e1n gi\u00e1m s\u00e1t: B\u1ea3ng v\u00e0 danh s\u00e1ch<\/h2>\n<p>C\u00e1c c\u00e1ch ti\u1ebfp c\u1eadn kh\u00e1c nhau \u0111\u1ed1i v\u1edbi vi\u1ec7c h\u1ecdc b\u00e1n gi\u00e1m s\u00e1t c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c nh\u00f3m l\u1ea1i th\u00e0nh:<\/p>\n<table>\n<thead>\n<tr>\n<th>Ti\u1ebfp c\u1eadn<\/th>\n<th>S\u1ef1 mi\u00eau t\u1ea3<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>M\u00f4 h\u00ecnh s\u00e1ng t\u1ea1o<\/td>\n<td>M\u00f4 h\u00ecnh ph\u00e2n ph\u1ed1i d\u1eef li\u1ec7u chung c\u01a1 b\u1ea3n<\/td>\n<\/tr>\n<tr>\n<td>T\u1ef1 h\u1ecdc<\/td>\n<td>M\u00f4 h\u00ecnh g\u1eafn nh\u00e3n d\u1eef li\u1ec7u c\u1ee7a ch\u00ednh n\u00f3<\/td>\n<\/tr>\n<tr>\n<td>\u0110a phi\u00ean b\u1ea3n<\/td>\n<td>S\u1eed d\u1ee5ng c\u00e1c t\u00fai phi\u00ean b\u1ea3n \u0111\u01b0\u1ee3c d\u00e1n nh\u00e3n m\u1ed9t ph\u1ea7n<\/td>\n<\/tr>\n<tr>\n<td>Ph\u01b0\u01a1ng ph\u00e1p d\u1ef1a tr\u00ean \u0111\u1ed3 th\u1ecb<\/td>\n<td>S\u1eed d\u1ee5ng bi\u1ec3u di\u1ec5n \u0111\u1ed3 th\u1ecb c\u1ee7a d\u1eef li\u1ec7u<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>C\u00e1c c\u00e1ch s\u1eed d\u1ee5ng H\u1ecdc t\u1eadp b\u00e1n gi\u00e1m s\u00e1t, c\u00e1c v\u1ea5n \u0111\u1ec1 v\u00e0 gi\u1ea3i ph\u00e1p c\u1ee7a ch\u00fang<\/h2>\n<h3>C\u00e1c \u1ee9ng d\u1ee5ng<\/h3>\n<ul>\n<li>Nh\u1eadn d\u1ea1ng h\u00ecnh \u1ea3nh<\/li>\n<li>Ph\u00e2n t\u00edch l\u1eddi n\u00f3i<\/li>\n<li>X\u1eed l\u00fd ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean<\/li>\n<li>Ch\u1ea9n \u0111o\u00e1n y t\u1ebf<\/li>\n<\/ul>\n<h3>V\u1ea5n \u0111\u1ec1 &amp; Gi\u1ea3i ph\u00e1p<\/h3>\n<ul>\n<li><strong>V\u1ea5n \u0111\u1ec1<\/strong>: Nhi\u1ec5u trong d\u1eef li\u1ec7u ch\u01b0a \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n.<br \/>\n<strong>Gi\u1ea3i ph\u00e1p<\/strong>: S\u1eed d\u1ee5ng ng\u01b0\u1ee1ng tin c\u1eady v\u00e0 c\u00e1c thu\u1eadt to\u00e1n m\u1ea1nh m\u1ebd.<\/li>\n<li><strong>V\u1ea5n \u0111\u1ec1<\/strong>: Gi\u1ea3 \u0111\u1ecbnh kh\u00f4ng ch\u00ednh x\u00e1c v\u1ec1 ph\u00e2n ph\u1ed1i d\u1eef li\u1ec7u.<br \/>\n<strong>Gi\u1ea3i ph\u00e1p<\/strong>: \u00c1p d\u1ee5ng ki\u1ebfn th\u1ee9c chuy\u00ean m\u00f4n v\u1ec1 l\u0129nh v\u1ef1c \u0111\u1ec3 h\u01b0\u1edbng d\u1eabn l\u1ef1a ch\u1ecdn m\u00f4 h\u00ecnh.<\/li>\n<\/ul>\n<h2>C\u00e1c \u0111\u1eb7c \u0111i\u1ec3m ch\u00ednh v\u00e0 nh\u1eefng so s\u00e1nh kh\u00e1c v\u1edbi c\u00e1c thu\u1eadt ng\u1eef t\u01b0\u01a1ng t\u1ef1<\/h2>\n<table>\n<thead>\n<tr>\n<th>T\u00ednh n\u0103ng<\/th>\n<th>Gi\u00e1m s\u00e1t<\/th>\n<th>B\u00e1n gi\u00e1m s\u00e1t<\/th>\n<th>Kh\u00f4ng \u0111\u01b0\u1ee3c gi\u00e1m s\u00e1t<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>S\u1eed d\u1ee5ng d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c d\u00e1n nh\u00e3n<\/td>\n<td>\u0110\u00fang<\/td>\n<td>\u0110\u00fang<\/td>\n<td>KH\u00d4NG<\/td>\n<\/tr>\n<tr>\n<td>S\u1eed d\u1ee5ng d\u1eef li\u1ec7u ch\u01b0a \u0111\u01b0\u1ee3c g\u1eafn nh\u00e3n<\/td>\n<td>KH\u00d4NG<\/td>\n<td>\u0110\u00fang<\/td>\n<td>\u0110\u00fang<\/td>\n<\/tr>\n<tr>\n<td>\u0110\u1ed9 ph\u1ee9c t\u1ea1p v\u00e0 chi ph\u00ed<\/td>\n<td>Cao<\/td>\n<td>V\u1eeba ph\u1ea3i<\/td>\n<td>Th\u1ea5p<\/td>\n<\/tr>\n<tr>\n<td>Hi\u1ec7u su\u1ea5t v\u1edbi nh\u00e3n gi\u1edbi h\u1ea1n<\/td>\n<td>Th\u1ea5p<\/td>\n<td>Cao<\/td>\n<td>Kh\u00e1c nhau<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Quan \u0111i\u1ec3m v\u00e0 c\u00f4ng ngh\u1ec7 c\u1ee7a t\u01b0\u01a1ng lai li\u00ean quan \u0111\u1ebfn h\u1ecdc t\u1eadp b\u00e1n gi\u00e1m s\u00e1t<\/h2>\n<p>T\u01b0\u01a1ng lai c\u1ee7a h\u1ecdc b\u00e1n gi\u00e1m s\u00e1t c\u00f3 v\u1ebb \u0111\u1ea7y h\u1ee9a h\u1eb9n v\u1edbi c\u00e1c nghi\u00ean c\u1ee9u \u0111ang di\u1ec5n ra t\u1eadp trung v\u00e0o:<\/p>\n<ul>\n<li>C\u00e1c thu\u1eadt to\u00e1n t\u1ed1t h\u01a1n \u0111\u1ec3 gi\u1ea3m ti\u1ebfng \u1ed3n<\/li>\n<li>T\u00edch h\u1ee3p v\u1edbi c\u00e1c framework deep learning<\/li>\n<li>M\u1edf r\u1ed9ng \u1ee9ng d\u1ee5ng tr\u00ean nhi\u1ec1u l\u0129nh v\u1ef1c c\u00f4ng nghi\u1ec7p kh\u00e1c nhau<\/li>\n<li>C\u00e1c c\u00f4ng c\u1ee5 n\u00e2ng cao cho kh\u1ea3 n\u0103ng di\u1ec5n gi\u1ea3i m\u00f4 h\u00ecnh<\/li>\n<\/ul>\n<h2>C\u00e1ch s\u1eed d\u1ee5ng ho\u1eb7c li\u00ean k\u1ebft m\u00e1y ch\u1ee7 proxy v\u1edbi ho\u1ea1t \u0111\u1ed9ng h\u1ecdc t\u1eadp b\u00e1n gi\u00e1m s\u00e1t<\/h2>\n<p>C\u00e1c m\u00e1y ch\u1ee7 proxy gi\u1ed1ng nh\u01b0 c\u00e1c m\u00e1y ch\u1ee7 do OneProxy cung c\u1ea5p c\u00f3 th\u1ec3 c\u00f3 \u00edch trong c\u00e1c t\u00ecnh hu\u1ed1ng h\u1ecdc b\u00e1n gi\u00e1m s\u00e1t. H\u1ecd c\u00f3 th\u1ec3 h\u1ed7 tr\u1ee3 trong vi\u1ec7c:<\/p>\n<ul>\n<li>Thu th\u1eadp c\u00e1c b\u1ed9 d\u1eef li\u1ec7u l\u1edbn t\u1eeb nhi\u1ec1u ngu\u1ed3n kh\u00e1c nhau, \u0111\u1eb7c bi\u1ec7t khi c\u00f3 nhu c\u1ea7u v\u01b0\u1ee3t qua c\u00e1c gi\u1edbi h\u1ea1n khu v\u1ef1c.<\/li>\n<li>\u0110\u1ea3m b\u1ea3o quy\u1ec1n ri\u00eang t\u01b0 v\u00e0 b\u1ea3o m\u1eadt khi x\u1eed l\u00fd d\u1eef li\u1ec7u nh\u1ea1y c\u1ea3m.<\/li>\n<li>N\u00e2ng cao hi\u1ec7u su\u1ea5t h\u1ecdc t\u1eadp ph\u00e2n t\u00e1n b\u1eb1ng c\u00e1ch gi\u1ea3m \u0111\u1ed9 tr\u1ec5 v\u00e0 duy tr\u00ec k\u1ebft n\u1ed1i nh\u1ea5t qu\u00e1n.<\/li>\n<\/ul>\n<h2>Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n<ul>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/label_propagation.html\" target=\"_new\" rel=\"noopener nofollow\">H\u01b0\u1edbng d\u1eabn Scikit-Learn v\u1ec1 h\u1ecdc t\u1eadp b\u00e1n gi\u00e1m s\u00e1t<\/a><\/li>\n<li><a href=\"https:\/\/www.iro.umontreal.ca\/~bengioy\/yoshua_en\/research.html\" target=\"_new\" rel=\"noopener nofollow\">Nghi\u00ean c\u1ee9u c\u1ee7a Yoshua Bengio v\u1ec1 h\u1ecdc t\u1eadp b\u00e1n gi\u00e1m s\u00e1t<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/vn\/\" target=\"_new\" rel=\"noopener\">D\u1ecbch v\u1ee5 c\u1ee7a OneProxy \u0111\u1ec3 x\u1eed l\u00fd d\u1eef li\u1ec7u an to\u00e0n<\/a><\/li>\n<\/ul>\n<p>B\u1eb1ng c\u00e1ch kh\u00e1m ph\u00e1 c\u00e1c kh\u00eda c\u1ea1nh c\u1ee7a h\u1ecdc b\u00e1n gi\u00e1m s\u00e1t, h\u01b0\u1edbng d\u1eabn to\u00e0n di\u1ec7n n\u00e0y nh\u1eb1m m\u1ee5c \u0111\u00edch cung c\u1ea5p cho ng\u01b0\u1eddi \u0111\u1ecdc s\u1ef1 hi\u1ec3u bi\u1ebft v\u1ec1 c\u00e1c nguy\u00ean t\u1eafc c\u1ed1t l\u00f5i, ph\u01b0\u01a1ng ph\u00e1p, \u1ee9ng d\u1ee5ng v\u00e0 tri\u1ec3n v\u1ecdng trong t\u01b0\u01a1ng lai, bao g\u1ed3m c\u1ea3 s\u1ef1 li\u00ean k\u1ebft c\u1ee7a n\u00f3 v\u1edbi c\u00e1c d\u1ecbch v\u1ee5 nh\u01b0 c\u00e1c d\u1ecbch v\u1ee5 do OneProxy cung c\u1ea5p.<\/p>","protected":false},"featured_media":470457,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-478919","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Semi-Supervised Learning: A Comprehensive Guide<\/mark>","faq_items":[{"question":"What is Semi-Supervised Learning?","answer":"<p>Semi-supervised learning is a machine learning approach that combines both labeled and unlabeled data in the training process. This hybrid method bridges the gap between supervised learning, which relies solely on labeled data, and unsupervised learning, which operates without any labeled data. By leveraging both types of data, semi-supervised learning often achieves better performance.<\/p>"},{"question":"What are the key features of Semi-Supervised Learning?","answer":"<p>The key features of semi-supervised learning include its efficiency in utilizing large amounts of readily available unlabeled data, cost-effectiveness in reducing the need for extensive labeling, flexibility across various domains, and challenges such as handling noisy data and incorrect labeling.<\/p>"},{"question":"How does Semi-Supervised Learning work?","answer":"<p>Semi-supervised learning works by initially training on a small labeled dataset and then utilizing predictions on the larger unlabeled data. Through iterative refinement and retraining, the model incorporates confident predictions as new labeled data, enhancing the overall accuracy of the model.<\/p>"},{"question":"What types of Semi-Supervised Learning exist?","answer":"<p>There are several approaches to semi-supervised learning, including Generative Models, Self-Learning, Multi-Instance learning, and Graph-Based Methods. These methods vary in how they model the underlying relationships between labeled and unlabeled data.<\/p>"},{"question":"What are some applications and problems of Semi-Supervised Learning?","answer":"<p>Semi-supervised learning finds applications in image recognition, speech analysis, natural language processing, and medical diagnosis. Common problems include noise in the unlabeled data and incorrect assumptions about data distribution, with solutions like confidence thresholding and applying domain expertise to guide model selection.<\/p>"},{"question":"How do Semi-Supervised Learning and proxy servers like OneProxy relate?","answer":"<p>Proxy servers like OneProxy can be associated with semi-supervised learning by assisting in collecting large datasets, ensuring privacy and security in handling sensitive data, and enhancing the performance of distributed learning by reducing latency.<\/p>"},{"question":"What are the future perspectives of Semi-Supervised Learning?","answer":"<p>The future of semi-supervised learning is promising with ongoing research in areas such as better algorithms for noise reduction, integration with deep learning frameworks, expansion across various industry sectors, and the development of tools for model interpretability.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/478919","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\/478919\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/470457"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=478919"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}