{"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\/my\/wiki\/k-nn-k-nearest-neighbours\/","title":{"rendered":"k-NN (k-Jiran Terdekat)"},"content":{"rendered":"<p>Maklumat ringkas tentang k-NN (k-Nearest Neighbours)<\/p>\n<p>k-Nearest Neighbors (k-NN) ialah algoritma pembelajaran mudah, bukan parametrik dan malas yang digunakan untuk pengelasan dan regresi. Dalam masalah pengelasan, k-NN memberikan label kelas berdasarkan majoriti label kelas antara &#039;k&#039; jiran terdekat objek. Untuk regresi, ia memberikan nilai berdasarkan purata atau median nilai &#039;k&#039; jiran terdekatnya.<\/p>\n<h2>Sejarah asal usul k-NN (k-Nearest Neighbours) dan sebutan pertama mengenainya<\/h2>\n<p>Algoritma k-NN mempunyai akarnya dalam literatur pengecaman corak statistik. Konsep ini diperkenalkan oleh Evelyn Fix dan Joseph Hodges pada tahun 1951, menandakan permulaan teknik tersebut. Sejak itu, ia telah digunakan secara meluas merentasi domain yang berbeza kerana kesederhanaan dan keberkesanannya.<\/p>\n<h2>Maklumat terperinci tentang k-NN (k-Nearest Neighbours). Memperluas topik k-NN (k-Nearest Neighbours)<\/h2>\n<p>k-NN beroperasi dengan mengenal pasti &#039;k&#039; contoh latihan yang paling hampir dengan input yang diberikan dan membuat ramalan berdasarkan peraturan majoriti atau purata. Metrik jarak seperti jarak Euclidean, jarak Manhattan, atau jarak Minkowski sering digunakan untuk mengukur persamaan. Komponen utama k-NN ialah:<\/p>\n<ul>\n<li>Pilihan &#039;k&#039; (bilangan jiran untuk dipertimbangkan)<\/li>\n<li>Metrik jarak (cth, Euclidean, Manhattan)<\/li>\n<li>Peraturan keputusan (cth, undian majoriti, undian wajaran)<\/li>\n<\/ul>\n<h2>Struktur dalaman k-NN (k-Nearest Neighbours). Cara k-NN (k-Nearest Neighbours) berfungsi<\/h2>\n<p>Kerja k-NN boleh dipecahkan kepada langkah-langkah berikut:<\/p>\n<ol>\n<li><strong>Pilih nombor &#039;k&#039;<\/strong> \u2013 Pilih bilangan jiran untuk dipertimbangkan.<\/li>\n<li><strong>Pilih metrik jarak<\/strong> \u2013 Tentukan cara mengukur &#039;kedekatan&#039; kejadian.<\/li>\n<li><strong>Cari jiran-jiran terdekat<\/strong> \u2013 Kenal pasti sampel latihan &#039;k&#039; yang paling hampir dengan contoh baharu.<\/li>\n<li><strong>Buat ramalan<\/strong> \u2013 Untuk klasifikasi, gunakan undian majoriti. Untuk regresi, hitung min atau median.<\/li>\n<\/ol>\n<h2>Analisis ciri utama k-NN (k-Nearest Neighbours)<\/h2>\n<ul>\n<li><strong>Kesederhanaan<\/strong>: Mudah dilaksanakan dan difahami.<\/li>\n<li><strong>Fleksibiliti<\/strong>: Berfungsi dengan pelbagai metrik jarak dan boleh disesuaikan dengan jenis data yang berbeza.<\/li>\n<li><strong>Tiada Fasa Latihan<\/strong>: Secara langsung menggunakan data latihan semasa fasa ramalan.<\/li>\n<li><strong>Sensitif kepada Data Bising<\/strong>: Outlier dan hingar boleh menjejaskan prestasi.<\/li>\n<li><strong>Intensif Pengiraan<\/strong>: Memerlukan pengiraan jarak ke semua sampel dalam set data latihan.<\/li>\n<\/ul>\n<h2>Jenis k-NN (k-Jiran Terdekat)<\/h2>\n<p>Terdapat pelbagai varian k-NN, seperti:<\/p>\n<table>\n<thead>\n<tr>\n<th>taip<\/th>\n<th>Penerangan<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Standard k-NN<\/td>\n<td>Menggunakan berat seragam untuk semua jiran.<\/td>\n<\/tr>\n<tr>\n<td>K-NN berwajaran<\/td>\n<td>Memberi lebih berat kepada jiran yang lebih dekat, biasanya berdasarkan songsangan jarak.<\/td>\n<\/tr>\n<tr>\n<td>Suai k-NN<\/td>\n<td>Laraskan &#039;k&#039; secara dinamik berdasarkan struktur tempatan ruang input.<\/td>\n<\/tr>\n<tr>\n<td>Wajaran Tempatan k-NN<\/td>\n<td>Menggabungkan kedua-dua penyesuaian &#039;k&#039; dan pemberat jarak.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Cara menggunakan k-NN (k-Nearest Neighbours), masalah, dan penyelesaiannya yang berkaitan dengan penggunaan<\/h2>\n<ul>\n<li><strong>Penggunaan<\/strong>: Klasifikasi, Regresi, Sistem Pengesyoran, Pengecaman Imej.<\/li>\n<li><strong>Masalah<\/strong>: Kos pengiraan yang tinggi, Sensitif kepada ciri yang tidak berkaitan, Isu kebolehskalaan.<\/li>\n<li><strong>Penyelesaian<\/strong>: Pemilihan ciri, Pemberat jarak, Menggunakan struktur data yang cekap seperti KD-Trees.<\/li>\n<\/ul>\n<h2>Ciri-ciri utama dan perbandingan lain dengan istilah yang serupa<\/h2>\n<table>\n<thead>\n<tr>\n<th>Atribut<\/th>\n<th>k-NN<\/th>\n<th>Pokok Keputusan<\/th>\n<th>SVM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Jenis Model<\/td>\n<td>Malas Belajar<\/td>\n<td>Bersemangat Belajar<\/td>\n<td>Bersemangat Belajar<\/td>\n<\/tr>\n<tr>\n<td>Kerumitan Latihan<\/td>\n<td>rendah<\/td>\n<td>Sederhana<\/td>\n<td>tinggi<\/td>\n<\/tr>\n<tr>\n<td>Kerumitan Ramalan<\/td>\n<td>tinggi<\/td>\n<td>rendah<\/td>\n<td>Sederhana<\/td>\n<\/tr>\n<tr>\n<td>Sensitiviti kepada Bunyi<\/td>\n<td>tinggi<\/td>\n<td>Sederhana<\/td>\n<td>rendah<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Perspektif dan teknologi masa depan yang berkaitan dengan k-NN (k-Nearest Neighbours)<\/h2>\n<p>Kemajuan masa hadapan mungkin menumpukan pada mengoptimumkan k-NN untuk data besar, menyepadukan dengan model pembelajaran mendalam, meningkatkan keteguhan kepada hingar dan mengautomasikan pemilihan hiperparameter.<\/p>\n<h2>Bagaimana pelayan proksi boleh digunakan atau dikaitkan dengan k-NN (k-Nearest Neighbours)<\/h2>\n<p>Pelayan proksi, seperti yang disediakan oleh OneProxy, boleh memainkan peranan dalam aplikasi k-NN yang melibatkan pengikisan web atau pengumpulan data. Pengumpulan data melalui proksi memastikan tidak dikenali dan boleh menyediakan set data yang lebih pelbagai dan tidak berat sebelah untuk membina model k-NN yang teguh.<\/p>\n<h2>Pautan berkaitan<\/h2>\n<ul>\n<li><a href=\"https:\/\/scikit-learn.org\/stable\/modules\/neighbors.html\" target=\"_new\" rel=\"noopener nofollow\">Scikit-belajar Dokumentasi k-NN<\/a><\/li>\n<li><a href=\"https:\/\/en.wikipedia.org\/wiki\/K-nearest_neighbors_algorithm\" target=\"_new\" rel=\"noopener nofollow\">Halaman Wikipedia tentang Algoritma Jiran Terdekat k-<\/a><\/li>\n<li><a href=\"https:\/\/oneproxy.pro\/my\/\" target=\"_new\" rel=\"noopener\">OneProxy \u2013 Penyelesaian Pelayan Proksi<\/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\/my\/wp-json\/wp\/v2\/wiki\/477783","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/my\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/my\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/my\/wp-json\/wp\/v2\/wiki\/477783\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/my\/wp-json\/wp\/v2\/media\/468739"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/my\/wp-json\/wp\/v2\/media?parent=477783"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}