{"id":477783,"date":"2023-08-09T09:20:08","date_gmt":"2023-08-09T09:20:08","guid":{"rendered":""},"modified":"2023-09-05T11:15:24","modified_gmt":"2023-09-05T11:15:24","slug":"k-nn-k-nearest-neighbours","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/k-nn-k-nearest-neighbours\/","title":{"rendered":"k-NN (k-H\u00e0ng x\u00f3m g\u1ea7n nh\u1ea5t)"},"content":{"rendered":"<p>Th\u00f4ng tin t\u00f3m t\u1eaft v\u1ec1 k-NN (k-Nearest Neighbors)<\/p>\n<p>k-Nearest Neighbors (k-NN) l\u00e0 m\u1ed9t thu\u1eadt to\u00e1n h\u1ecdc \u0111\u01a1n gi\u1ea3n, kh\u00f4ng tham s\u1ed1 v\u00e0 l\u01b0\u1eddi bi\u1ebfng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 ph\u00e2n lo\u1ea1i v\u00e0 h\u1ed3i quy. Trong c\u00e1c b\u00e0i to\u00e1n ph\u00e2n lo\u1ea1i, k-NN g\u00e1n nh\u00e3n l\u1edbp d\u1ef1a tr\u00ean ph\u1ea7n l\u1edbn c\u00e1c nh\u00e3n l\u1edbp trong s\u1ed1 &#039;k&#039; l\u00e2n c\u1eadn g\u1ea7n nh\u1ea5t c\u1ee7a \u0111\u1ed1i t\u01b0\u1ee3ng. \u0110\u1ed1i v\u1edbi h\u1ed3i quy, n\u00f3 g\u00e1n m\u1ed9t gi\u00e1 tr\u1ecb d\u1ef1a tr\u00ean m\u1ee9c trung b\u00ecnh ho\u1eb7c trung v\u1ecb c\u1ee7a c\u00e1c gi\u00e1 tr\u1ecb c\u1ee7a &#039;k&#039; l\u00e2n c\u1eadn g\u1ea7n nh\u1ea5t c\u1ee7a n\u00f3.<\/p>\n<h2>L\u1ecbch s\u1eed ngu\u1ed3n g\u1ed1c c\u1ee7a k-NN (k-Nearest Neighbors) v\u00e0 l\u1ea7n \u0111\u1ea7u ti\u00ean nh\u1eafc t\u1edbi n\u00f3<\/h2>\n<p>Thu\u1eadt to\u00e1n k-NN c\u00f3 ngu\u1ed3n g\u1ed1c t\u1eeb t\u00e0i li\u1ec7u nh\u1eadn d\u1ea1ng m\u1eabu th\u1ed1ng k\u00ea. Kh\u00e1i ni\u1ec7m n\u00e0y \u0111\u01b0\u1ee3c Evelyn Fix v\u00e0 Joseph Hodges \u0111\u01b0a ra v\u00e0o n\u0103m 1951, \u0111\u00e1nh d\u1ea5u s\u1ef1 ra \u0111\u1eddi c\u1ee7a k\u1ef9 thu\u1eadt n\u00e0y. K\u1ec3 t\u1eeb \u0111\u00f3, n\u00f3 \u0111\u00e3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng r\u1ed9ng r\u00e3i tr\u00ean nhi\u1ec1u l\u0129nh v\u1ef1c kh\u00e1c nhau do t\u00ednh \u0111\u01a1n gi\u1ea3n v\u00e0 hi\u1ec7u qu\u1ea3 c\u1ee7a n\u00f3.<\/p>\n<h2>Th\u00f4ng tin chi ti\u1ebft v\u1ec1 k-NN (k-L\u00e1ng gi\u1ec1ng g\u1ea7n nh\u1ea5t). M\u1edf r\u1ed9ng ch\u1ee7 \u0111\u1ec1 k-NN (k-Nearest Neighbors)<\/h2>\n<p>k-NN ho\u1ea1t \u0111\u1ed9ng b\u1eb1ng c\u00e1ch x\u00e1c \u0111\u1ecbnh &#039;k&#039; v\u00ed d\u1ee5 hu\u1ea5n luy\u1ec7n g\u1ea7n nh\u1ea5t v\u1edbi \u0111\u1ea7u v\u00e0o nh\u1ea5t \u0111\u1ecbnh v\u00e0 \u0111\u01b0a ra d\u1ef1 \u0111o\u00e1n d\u1ef1a tr\u00ean quy t\u1eafc \u0111a s\u1ed1 ho\u1eb7c t\u00ednh trung b\u00ecnh. C\u00e1c s\u1ed1 li\u1ec7u kho\u1ea3ng c\u00e1ch nh\u01b0 kho\u1ea3ng c\u00e1ch Euclide, kho\u1ea3ng c\u00e1ch Manhattan ho\u1eb7c kho\u1ea3ng c\u00e1ch Minkowski th\u01b0\u1eddng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 \u0111o l\u01b0\u1eddng \u0111\u1ed9 t\u01b0\u01a1ng t\u1ef1. C\u00e1c th\u00e0nh ph\u1ea7n ch\u00ednh c\u1ee7a k-NN l\u00e0:<\/p>\n<ul>\n<li>L\u1ef1a ch\u1ecdn &#039;k&#039; (s\u1ed1 l\u01b0\u1ee3ng h\u00e0ng x\u00f3m c\u1ea7n xem x\u00e9t)<\/li>\n<li>S\u1ed1 li\u1ec7u kho\u1ea3ng c\u00e1ch (v\u00ed d\u1ee5: Euclidean, Manhattan)<\/li>\n<li>Quy t\u1eafc quy\u1ebft \u0111\u1ecbnh (v\u00ed d\u1ee5: bi\u1ec3u quy\u1ebft theo \u0111a s\u1ed1, bi\u1ec3u quy\u1ebft theo tr\u1ecdng s\u1ed1)<\/li>\n<\/ul>\n<h2>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a k-NN (k-H\u00e0ng x\u00f3m g\u1ea7n nh\u1ea5t). C\u00e1ch ho\u1ea1t \u0111\u1ed9ng c\u1ee7a k-NN (k-H\u00e0ng x\u00f3m g\u1ea7n nh\u1ea5t)<\/h2>\n<p>Ho\u1ea1t \u0111\u1ed9ng c\u1ee7a k-NN c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c chia th\u00e0nh c\u00e1c b\u01b0\u1edbc sau:<\/p>\n<ol>\n<li><strong>Ch\u1ecdn s\u1ed1 &#039;k&#039;<\/strong> \u2013 L\u1ef1a ch\u1ecdn s\u1ed1 l\u01b0\u1ee3ng l\u00e1ng gi\u1ec1ng c\u1ea7n xem x\u00e9t.<\/li>\n<li><strong>Ch\u1ecdn th\u01b0\u1edbc \u0111o kho\u1ea3ng c\u00e1ch<\/strong> \u2013 X\u00e1c \u0111\u1ecbnh c\u00e1ch \u0111o l\u01b0\u1eddng m\u1ee9c \u0111\u1ed9 \u201cg\u1ea7n g\u0169i\u201d c\u1ee7a c\u00e1c tr\u01b0\u1eddng h\u1ee3p.<\/li>\n<li><strong>T\u00ecm k h\u00e0ng x\u00f3m g\u1ea7n nh\u1ea5t<\/strong> \u2013 X\u00e1c \u0111\u1ecbnh &#039;k&#039; m\u1eabu hu\u1ea5n luy\u1ec7n g\u1ea7n nh\u1ea5t v\u1edbi phi\u00ean b\u1ea3n m\u1edbi.<\/li>\n<li><strong>L\u00e0m cho m\u1ed9t d\u1ef1 \u0111o\u00e1n<\/strong> \u2013 \u0110\u1ec3 ph\u00e2n lo\u1ea1i, h\u00e3y s\u1eed d\u1ee5ng bi\u1ec3u quy\u1ebft \u0111a s\u1ed1. \u0110\u1ec3 h\u1ed3i quy, h\u00e3y t\u00ednh gi\u00e1 tr\u1ecb trung b\u00ecnh ho\u1eb7c trung v\u1ecb.<\/li>\n<\/ol>\n<h2>Ph\u00e2n t\u00edch c\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a k-NN (k-Nearest Neighbors)<\/h2>\n<ul>\n<li><strong>S\u1ef1 \u0111\u01a1n gi\u1ea3n<\/strong>: D\u1ec5 th\u1ef1c hi\u1ec7n v\u00e0 d\u1ec5 hi\u1ec3u.<\/li>\n<li><strong>Uy\u1ec3n chuy\u1ec3n<\/strong>: Ho\u1ea1t \u0111\u1ed9ng v\u1edbi nhi\u1ec1u s\u1ed1 li\u1ec7u kho\u1ea3ng c\u00e1ch kh\u00e1c nhau v\u00e0 c\u00f3 th\u1ec3 th\u00edch \u1ee9ng v\u1edbi c\u00e1c lo\u1ea1i d\u1eef li\u1ec7u kh\u00e1c nhau.<\/li>\n<li><strong>Kh\u00f4ng c\u00f3 giai \u0111o\u1ea1n \u0111\u00e0o t\u1ea1o<\/strong>: Tr\u1ef1c ti\u1ebfp s\u1eed d\u1ee5ng d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n trong giai \u0111o\u1ea1n d\u1ef1 \u0111o\u00e1n.<\/li>\n<li><strong>Nh\u1ea1y c\u1ea3m v\u1edbi d\u1eef li\u1ec7u \u1ed3n \u00e0o<\/strong>: C\u00e1c ngo\u1ea1i l\u1ec7 v\u00e0 ti\u1ebfng \u1ed3n c\u00f3 th\u1ec3 \u1ea3nh h\u01b0\u1edfng \u0111\u1ebfn hi\u1ec7u su\u1ea5t.<\/li>\n<li><strong>T\u00ednh to\u00e1n chuy\u00ean s\u00e2u<\/strong>: Y\u00eau c\u1ea7u t\u00ednh to\u00e1n kho\u1ea3ng c\u00e1ch \u0111\u1ebfn t\u1ea5t c\u1ea3 c\u00e1c m\u1eabu trong t\u1eadp d\u1eef li\u1ec7u hu\u1ea5n luy\u1ec7n.<\/li>\n<\/ul>\n<h2>C\u00e1c lo\u1ea1i k-NN (k-L\u00e1ng gi\u1ec1ng g\u1ea7n nh\u1ea5t)<\/h2>\n<p>C\u00f3 nhi\u1ec1u bi\u1ebfn th\u1ec3 kh\u00e1c nhau c\u1ee7a k-NN, ch\u1eb3ng h\u1ea1n nh\u01b0:<\/p>\n<table>\n<thead>\n<tr>\n<th>Ki\u1ec3u<\/th>\n<th>S\u1ef1 mi\u00eau t\u1ea3<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>chu\u1ea9n k-NN<\/td>\n<td>S\u1eed d\u1ee5ng tr\u1ecdng l\u01b0\u1ee3ng \u0111\u1ed3ng \u0111\u1ec1u cho t\u1ea5t c\u1ea3 h\u00e0ng x\u00f3m.<\/td>\n<\/tr>\n<tr>\n<td>k-NN c\u00f3 tr\u1ecdng s\u1ed1<\/td>\n<td>Mang l\u1ea1i nhi\u1ec1u tr\u1ecdng l\u01b0\u1ee3ng h\u01a1n cho nh\u1eefng ng\u01b0\u1eddi h\u00e0ng x\u00f3m g\u1ea7n h\u01a1n, th\u01b0\u1eddng d\u1ef1a tr\u00ean ngh\u1ecbch \u0111\u1ea3o c\u1ee7a kho\u1ea3ng c\u00e1ch.<\/td>\n<\/tr>\n<tr>\n<td>k-NN th\u00edch \u1ee9ng<\/td>\n<td>\u0110i\u1ec1u ch\u1ec9nh \u0111\u1ed9ng &#039;k&#039; d\u1ef1a tr\u00ean c\u1ea5u tr\u00fac c\u1ee5c b\u1ed9 c\u1ee7a kh\u00f4ng gian \u0111\u1ea7u v\u00e0o.<\/td>\n<\/tr>\n<tr>\n<td>k-NN c\u00f3 tr\u1ecdng s\u1ed1 c\u1ee5c b\u1ed9<\/td>\n<td>K\u1ebft h\u1ee3p c\u1ea3 &#039;k&#039; th\u00edch \u1ee9ng v\u00e0 tr\u1ecdng s\u1ed1 kho\u1ea3ng c\u00e1ch.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>C\u00e1c c\u00e1ch s\u1eed d\u1ee5ng k-NN (k-Nearest Neighbors), c\u00e1c v\u1ea5n \u0111\u1ec1 v\u00e0 gi\u1ea3i ph\u00e1p li\u00ean quan \u0111\u1ebfn vi\u1ec7c s\u1eed d\u1ee5ng<\/h2>\n<ul>\n<li><strong>C\u00e1ch s\u1eed d\u1ee5ng<\/strong>: Ph\u00e2n lo\u1ea1i, H\u1ed3i quy, H\u1ec7 th\u1ed1ng g\u1ee3i \u00fd, Nh\u1eadn d\u1ea1ng h\u00ecnh \u1ea3nh.<\/li>\n<li><strong>C\u00e1c v\u1ea5n \u0111\u1ec1<\/strong>: Chi ph\u00ed t\u00ednh to\u00e1n cao, Nh\u1ea1y c\u1ea3m v\u1edbi c\u00e1c t\u00ednh n\u0103ng kh\u00f4ng li\u00ean quan, C\u00e1c v\u1ea5n \u0111\u1ec1 v\u1ec1 kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng.<\/li>\n<li><strong>C\u00e1c gi\u1ea3i ph\u00e1p<\/strong>: L\u1ef1a ch\u1ecdn t\u00ednh n\u0103ng, Tr\u1ecdng s\u1ed1 kho\u1ea3ng c\u00e1ch, S\u1eed d\u1ee5ng c\u00e1c c\u1ea5u tr\u00fac d\u1eef li\u1ec7u hi\u1ec7u qu\u1ea3 nh\u01b0 C\u00e2y KD.<\/li>\n<\/ul>\n<h2>C\u00e1c \u0111\u1eb7c \u0111i\u1ec3m ch\u00ednh v\u00e0 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>Thu\u1ed9c t\u00ednh<\/th>\n<th>k-NN<\/th>\n<th>C\u00e2y quy\u1ebft \u0111\u1ecbnh<\/th>\n<th>SVM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Lo\u1ea1i m\u00f4 h\u00ecnh<\/td>\n<td>L\u01b0\u1eddi h\u1ecdc<\/td>\n<td>H\u00e1o h\u1ee9c h\u1ecdc t\u1eadp<\/td>\n<td>H\u00e1o h\u1ee9c h\u1ecdc t\u1eadp<\/td>\n<\/tr>\n<tr>\n<td>\u0110\u1ed9 ph\u1ee9c t\u1ea1p \u0111\u00e0o t\u1ea1o<\/td>\n<td>Th\u1ea5p<\/td>\n<td>Trung b\u00ecnh<\/td>\n<td>Cao<\/td>\n<\/tr>\n<tr>\n<td>\u0110\u1ed9 ph\u1ee9c t\u1ea1p d\u1ef1 \u0111o\u00e1n<\/td>\n<td>Cao<\/td>\n<td>Th\u1ea5p<\/td>\n<td>Trung b\u00ecnh<\/td>\n<\/tr>\n<tr>\n<td>Nh\u1ea1y c\u1ea3m v\u1edbi ti\u1ebfng \u1ed3n<\/td>\n<td>Cao<\/td>\n<td>Trung b\u00ecnh<\/td>\n<td>Th\u1ea5p<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>C\u00e1c quan \u0111i\u1ec3m v\u00e0 c\u00f4ng ngh\u1ec7 trong t\u01b0\u01a1ng lai li\u00ean quan \u0111\u1ebfn k-NN (k-Nearest Neighbors)<\/h2>\n<p>Nh\u1eefng ti\u1ebfn b\u1ed9 trong t\u01b0\u01a1ng lai c\u00f3 th\u1ec3 t\u1eadp trung v\u00e0o vi\u1ec7c t\u1ed1i \u01b0u h\u00f3a k-NN cho d\u1eef li\u1ec7u l\u1edbn, t\u00edch h\u1ee3p v\u1edbi c\u00e1c m\u00f4 h\u00ecnh h\u1ecdc s\u00e2u, t\u0103ng c\u01b0\u1eddng kh\u1ea3 n\u0103ng ch\u1ed1ng nhi\u1ec5u v\u00e0 t\u1ef1 \u0111\u1ed9ng h\u00f3a vi\u1ec7c l\u1ef1a ch\u1ecdn si\u00eau tham s\u1ed1.<\/p>\n<h2>C\u00e1ch s\u1eed d\u1ee5ng ho\u1eb7c li\u00ean k\u1ebft m\u00e1y ch\u1ee7 proxy v\u1edbi k-NN (K-H\u00e0ng x\u00f3m g\u1ea7n nh\u1ea5t)<\/h2>\n<p>C\u00e1c m\u00e1y ch\u1ee7 proxy, ch\u1eb3ng h\u1ea1n nh\u01b0 c\u00e1c m\u00e1y ch\u1ee7 do OneProxy cung c\u1ea5p, c\u00f3 th\u1ec3 \u0111\u00f3ng m\u1ed9t vai tr\u00f2 trong c\u00e1c \u1ee9ng d\u1ee5ng k-NN li\u00ean quan \u0111\u1ebfn vi\u1ec7c qu\u00e9t web ho\u1eb7c thu th\u1eadp d\u1eef li\u1ec7u. Vi\u1ec7c thu th\u1eadp d\u1eef li\u1ec7u th\u00f4ng qua proxy \u0111\u1ea3m b\u1ea3o t\u00ednh \u1ea9n danh v\u00e0 c\u00f3 th\u1ec3 cung c\u1ea5p c\u00e1c b\u1ed9 d\u1eef li\u1ec7u \u0111a d\u1ea1ng v\u00e0 kh\u00f4ng thi\u00ean v\u1ecb h\u01a1n \u0111\u1ec3 x\u00e2y d\u1ef1ng c\u00e1c m\u00f4 h\u00ecnh k-NN m\u1ea1nh m\u1ebd.<\/p>\n<h2>Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n<ul>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/neighbors.html\" target=\"_new\" rel=\"noopener nofollow\">T\u00e0i li\u1ec7u Scikit-learn k-NN<\/a><\/li>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/K-nearest_neighbors_algorithm\" target=\"_new\" rel=\"noopener nofollow\">Trang Wikipedia v\u1ec1 thu\u1eadt to\u00e1n h\u00e0ng x\u00f3m g\u1ea7n nh\u1ea5t k<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/vn\/\" target=\"_new\" rel=\"noopener\">OneProxy \u2013 Gi\u1ea3i ph\u00e1p m\u00e1y ch\u1ee7 proxy<\/a><\/li>\n<\/ul>","protected":false},"featured_media":468739,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477783","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>k-NN (k-Nearest Neighbours)<\/mark>","faq_items":[{"question":"What is the k-Nearest Neighbours (k-NN) algorithm?","answer":"<p>The k-Nearest Neighbours (k-NN) is a simple and non-parametric algorithm used for classification and regression. It works by identifying the 'k' closest training examples to a given input and making predictions based on majority rule or averaging.<\/p>"},{"question":"What was the origin of the k-NN algorithm?","answer":"<p>The k-NN algorithm was introduced by Evelyn Fix and Joseph Hodges in 1951, marking its inception in statistical pattern recognition literature.<\/p>"},{"question":"How does the k-NN algorithm work?","answer":"<p>The k-NN algorithm works by choosing a number 'k', selecting a distance metric, finding the k-nearest neighbors to the new instance, and making a prediction based on majority voting for classification or computing the mean or median for regression.<\/p>"},{"question":"What are the key features of the k-NN algorithm?","answer":"<p>Key features of k-NN include its simplicity, flexibility, lack of a training phase, sensitivity to noisy data, and computational intensity.<\/p>"},{"question":"What are the different types of k-NN?","answer":"<p>There are various types of k-NN, including Standard k-NN, Weighted k-NN, Adaptive k-NN, and Locally Weighted k-NN.<\/p>"},{"question":"How can k-NN be used, and what are the related problems and solutions?","answer":"<p>k-NN can be used for classification, regression, recommender systems, and image recognition. Common problems include high computation cost, sensitivity to irrelevant features, and scalability issues. Solutions may involve feature selection, distance weighting, and utilizing efficient data structures like KD-Trees.<\/p>"},{"question":"How does the k-NN algorithm compare with other similar terms?","answer":"<p>k-NN differs from other algorithms like Decision Trees and SVM in aspects such as model type, training complexity, prediction complexity, and sensitivity to noise.<\/p>"},{"question":"What are the future prospects of k-NN?","answer":"<p>Future advancements in k-NN may focus on optimizing for big data, integrating with deep learning models, enhancing robustness to noise, and automating hyperparameter selection.<\/p>"},{"question":"How are proxy servers like OneProxy associated with k-NN?","answer":"<p>Proxy servers like OneProxy can be used in k-NN applications for web scraping or data collection. Gathering data through proxies ensures anonymity and can provide more diverse and unbiased datasets for building robust k-NN models.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/477783","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\/477783\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/468739"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=477783"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}