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other provinces and regions in Vietnam and other country.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Address\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Headquarters of Can Tho Journal of Medicine and Pharmacy, located Scientific Research and International Cooperation Office: 179 Nguyen Van Cu Street, An Khanh Ward, Ninh Kieu District, Can Tho City, Vietnam.\u003C\u002Fspan>\u003C\u002Fp>","\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Ngày 16\u002F7\u002F2015, Tạp chí Y Dược học Cần Thơ được cấp chỉ số quốc tế: ISSN 2354-1210.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Từ tháng 4\u002F2016, Tạp chí đã được Hội đồng Giáo sư ngành Y đưa vào danh sách các tạp chí khoa học Y học được tính điểm công trình 0-0,5 điểm cho một bài báo đăng.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Năm 2020 Tạp chí Y Dược học Cần Thơ đã được phê duyệt vào danh mục của các Hội đồng Giáo sư ngành Dược học được tính điểm công trình 0-0,5 điểm cho một bài báo đăng.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí Y Dược học Cần Thơ ra 12 số\u002Fnăm, 180-200 trang\u002Fsố.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Từ tháng 12\u002F2022 Tạp chí Y Dược học Cần Thơ là thành viên của hệ thống Crossref và từ tháng 01\u002F2023 tạp chí thực hiện bình duyệt online kín 2 chiều nhằm tăng tính minh bạch, tin cậy của các công trình nghiên cứu khoa học và đảm bảo tốt nhất chất lượng khoa học của bài viết.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tôn chỉ, mục đích và phạm vi của tạp chí\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tôn chỉ và mục đích hoạt động của tạp chí: xuất bản nhằm mục đích phổ biến kết quả từ các đề tài nghiên cứu khoa học; giao lưu trao đổi khoa học, chia sẻ kinh nghiệm, học tập, đồng thời cập nhật thông tin khoa học mới trong các lĩnh vực y, sinh, dược học trong và ngoài nước.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Phạm vi của tạp chí: Tạp chí xuất bản được chia thành 3 chuyên mục: (i) Bài báo nghiên cứu khoa học là kết quả công trình nghiên cứu khoa học có giá trị đã được triển khai nghiên cứu, (ii) Bài tổng quan y, sinh, dược học: phục vụ mục tiêu đào tạo liên tục trong lĩnh vực y, sinh, dược học; nhằm hệ thống hóa những kiến thức kinh điển và hiện đại; (iii) Thông tin cập nhật kiến thức mới về y, sinh, dược học trong nước và trên thế giới.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Chính sách truy cập mở\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí Y Dược học Cần Thơ áp dụng chính sách truy cập mở đối với các bài báo đã xuất bản đến với độc giả, nhằm mở rộng cơ hội tiếp cận các kết quả nghiên cứu chất lượng cao và tăng cường trao đổi kiến thức. Tạp chí đăng tải trực tuyến (miễn phí) toàn văn các bài báo được công bố trên website của Tạp chí (https:\u002F\u002Ftapchi.ctump.edu.vn).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Đạo đức xuất bản\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí Y Dược học Cần Thơ cam kết tuân thủ đạo đức xuất bản phù hợp với các hướng dẫn và tiêu chuẩn của the Committee on Publication Ethics (COPE), tuân thủ các nguyên tắc của COPE’s Core Practices, Best Practices Guidelines for Journal Editors và Guidelines on Good Publication Practices.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Bản thảo bài báo chỉ được chấp nhận khi được tác giả chịu trách nhiệm chính cam kết các nội dung sau: Các nội dung của bản thảo chưa được đăng tải toàn bộ hoặc một phần ở các tạp chí khác; Tất cả các tác giả đều có đóng góp một cách đáng kể vào quá trình nghiên cứu hoặc chuẩn bị bản thảo và cùng chịu trách nhiệm về các nội dung của bản thảo; Tuân thủ các biện pháp đảm bảo đạo đức nghiên cứu (ví dụ thỏa thuận đồng ý tham gia nghiên cứu).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Cam kết bảo mật\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí cam kết thực hiện và tuân thủ các quy định của luật và các văn bản hướng dẫn liên quan đến bảo mật thông tin cá nhân trên không gian mạng. Các thông tin mà người dùng (tác giả, độc giả, biên tập viên, người phản biện) nhập vào các biểu mẫu trên Hệ thống Quản lý xuất bản trực tuyến của tạp chí chỉ được sử dụng vào các mục đích đã được tuyên bố rõ ràng và sẽ không được cung cấp cho bất kỳ bên thứ ba nào khác, hay dùng vào bất kỳ mục đích nào khác.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Phí gửi bài\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Lệ phí gửi đăng bài: 1.000.000đ\u002Fbài báo\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Lệ phí gửi đăng nhanh: 1.500.000đ\u002Fbài báo\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Đối với tác giả là cán bộ viên chức thuộc Trường Đại học Y Dược Cần Thơ thì được hỗ trợ 50% lệ phí gửi đăng bài.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Đối với sinh viên thực hiện đề tài nghiên cứu khoa học cấp trường được hỗ trợ 100% lệ phí đăng bài ( Tác giả gửi đính kèm “ Quyết định về việc giao tổ chức thực hiện đề tài nghiên cứu khoa học cấp Trường của sinh viên”).\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Hình thức nộp lệ phí:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">1. Tiền mặt:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Nộp trực tiếp tại Phòng Tài chính - Kế toán, Trường Đại học Y Dược Cần Thơ, số 179 Nguyễn Văn Cừ, P. An Khánh, Q. Ninh Kiều, thành phố Cần Thơ.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">2. Chuyển khoản:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tên Tài khoản: Trường ĐHYD Cần Thơ, Số TK: 0111000115668, tại ngân hàng Vietcombank chi nhánh Cần Thơ.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Thời gian: Áp dụng từ ngày 01\u002F02\u002F2023.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">* Phí gửi bài không được hoàn trả khi bài viết bị từ chối hoặc tác giả xin rút bài viết.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Quy trình phản biện bài báo\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tạp chí Y Dược học Cần Thơ thực hiện quy trình phản biện kín hai chiều nghiêm ngặt. Danh tính của những người phản biện không được tiết lộ cho các tác giả và ngược lại. Quy trình thẩm định bài báo đăng gồm các bước sau:\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tiếp nhận bản thảo\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Tác giả liên hệ gửi bản thảo đến Tạp chí qua hệ thống trực tuyến tại website: https:\u002F\u002Ftapchi.ctump.edu.vn. Hướng dẫn về cách đăng ký, gửi bài và chuẩn bị bản thảo được cung cấp trên website của Tạp chí.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Sàng lọc sơ bộ\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Sau khi Tòa soạn nhận được bài báo của tác giả, Ban Thư ký sẽ tiến hành kiểm tra sơ bộ bài báo (các yêu cầu về nội dung và hình thức). Những bài báo không đúng quy cách hoặc có nội dung không phù hợp hoặc vi phạm bản quyền sẽ bị từ chối (Ban Thư ký thông báo phản hồi đến tác giả trong vòng 1 tuần). Những bài báo đủ điều kiện, được Ban Thư ký tòa soạn chuyển đến Ban Biên tập có cùng chuyên môn với nội dung bài báo để đề xuất người phản biện. Thời gian kể từ khi Ban Biên tập nhận bài báo đến khi đề xuất người phản biện bài báo chậm nhất là 5 ngày.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Vòng phản biện\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">1. Ban Thư ký gửi bài và yêu cầu phản biện đến 02 phản biện độc lập.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">2. Các phản biện gởi nhận xét cho Ban Thư ký. Thời gian từ khi gửi bài cho phản biện đến khi nhận ý kiến của phản biện tối đa là 20 ngày.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Xử ký kết quả phản biện\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">1. Nếu ý kiến đồng ý cho đăng và không cần chỉnh sửa, Ban Thư ký tiếp tục đăng bài theo qui trình.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">2. Nếu ý kiến đồng ý đăng và cần chỉnh sửa, Ban Thư ký sẽ thông tin đến tác giả chỉnh sửa theo yêu cầu của người phản biện. Thời gian chỉnh sửa và gửi lại kéo dài không quá 2 tuần, từ khi tác giả bài báo nhận được thông tin (Quá trình này có thể lặp lại tối đa 2 lần\u002F1 bài báo). Khi có sự thống nhất, đồng ý của người phản biện; bài báo được tiếp tục đăng theo qui trình.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">3. Những bài báo có chất lượng không đạt yêu cầu, cả 2 phản biện không đồng ý cho đăng sẽ bị Tòa soạn từ chối đăng.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">Xuất bản\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">1. Ban Thư ký tổng hợp các bản thảo đã được tác giả hoàn thiện sau thẩm định trình Ban Biên tập xem xét, Tổng Biên tập phê duyệt, quyết định bài đăng theo các tiêu chí: sự phù hợp nội dung với tôn chỉ và mục đích, thể loại bài viết (ưu tiên các bài có bài có nghiên cứu chuyên sâu, hàm lượng khoa học cao), đóng góp mới bài báo, bài báo được ưu tiên đăng trong số gần nhất của Tạp chí theo thứ tự: tính thời sự, chất lượng bài báo và thời gian gửi bài.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">2. Ban Biên tập và Ban Thư ký biên tập bản thảo, chế bản, đọc rà soát lỗi. Thời gian hoàn thành từ 10-15 ngày.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">3. Ban Thư ký có trách nhiệm thông báo cho tác giả bài báo (bằng e-mail) về tình hình phê duyệt bài báo, thời gian, số kỳ, tập xuất bản bài báo theo qui định.\u003C\u002Fspan>\u003C\u002Fp>\u003Cp>\u003Cbr>\u003C\u002Fp>\u003Cp>\u003Cspan style=\"color: rgb(0, 0, 0);\">4. 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R. E. (1990). The strength of weak learnability. Machine Learning 5 197-227.",{"doi":2035},"10.1007\u002FBF00116037",{"id":26,"text":1078,"url":26,"identifiers":2037},{},{"id":26,"text":2039,"url":26,"identifiers":2040},"Hastie, T., Tibshirani, R. and Buja, A. (1994). Flexible discriminant analysis by optimal scoring. J. Amer. Statist. Assoc. 89 1255-1270.",{"doi":2041},"10.2307\u002F2290989",{"id":26,"text":2043,"url":26,"identifiers":2044},"Buja, A., Hastie, T. and Tibshirani, R. (1989). Linear smoothers and additive models (with discussion). Ann. Statist. 17 453-555.",{"doi":2045},"10.1214\u002Faos\u002F1176347124",{"id":26,"text":2047,"url":26,"identifiers":2048},"Breiman, L. (1996). Bagging predictors. Machine Learning 24 123-140.",{"doi":1577},{"id":26,"text":2047,"url":26,"identifiers":2050},{"doi":1577},{"id":26,"text":2052,"url":26,"identifiers":2053},"Breiman, L., Friedman, J., Olshen, R. and Stone, C. (1984). Classification and Regression Trees. Wadsworth, Belmont, CA.",{},{"id":26,"text":2055,"url":26,"identifiers":2056},"Friedman, J. (1991). Multivariate adaptive regression splines (with discussion). Ann. Statist. 19 1-141.",{"doi":1087},{"id":26,"text":2058,"url":26,"identifiers":2059},"Friedman, J. and Stuetzle, W. (1981). Projection pursuit regression. J. Amer. Statist. Assoc. 76 817-823.",{"doi":2060},"10.2307\u002F2287576",{"id":26,"text":2058,"url":26,"identifiers":2062},{"doi":2060},{"id":26,"text":2064,"url":26,"identifiers":2065},"Holte, R. (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning 11 63-90.",{"doi":2066},"10.1023\u002FA:1022631118932",{"id":26,"text":2068,"url":26,"identifiers":2069},"McCullagh, P. and Nelder, J. (1989). Generalized Linear Models. Chapman and Hall, London.",{"doi":1638},{"id":26,"text":2071,"url":26,"identifiers":2072},"Valiant, L. G. (1984). A theory of the learnable. Comm. ACM 27 1134-1142.",{"doi":2073},"10.1145\u002F1968.1972",{"id":26,"text":2075,"url":26,"identifiers":2076},"in a paper by Amit and Geman (1997). Using this approach and 100 iterations gives the following test-set errors as compared to the best corresponding values for LogitBoost.",{},{"id":26,"text":2078,"url":26,"identifiers":2079},"Wheway, V. (1999). Variance reduction trends on `boosted' classifiers. Available from virg@cse. unsw.edu.au",{},{"id":26,"text":2081,"url":26,"identifiers":2082},"phenomena are reported in Friedman (1999). From our own work [B ¨uhlmann and Yu (2000)] we know that stumps evaluated at x have high variances for x in a whole region of the covariate space. From an asymptotic point of view, this region is \"centered around\" the true optimal split point for a stump and has \"substantial\" size O n-1\u002F3 . That is, stumps do have high variances even in low dimensions as in this simple case (with only three parameters) as long as one is looking at the \"right scale\" O n-1\u002F3 ; such a high variance presumably propagates when combining stumps in boosting. This observation is the starting point for another boosting machine to be described next.",{},{"id":26,"text":2084,"url":26,"identifiers":2085},"Hastie, T. and Tibshirani, R. (1990). Generalized Additive Models. Chapman and Hall, London. Seminar f ¨ur Statistik ETH-Zentrum, LEO D72 CH-8092 Zurich Switzerland E-mail buhlmann@stat.math.ethz.ch Department of Statistics University of California Berkeley, California 94720-3860",{},{"id":26,"text":2087,"url":26,"identifiers":2088},"proposed by Freund and Mason (1999). They represent decision trees as sums of very simple functions and use boosting to simultaneously learn both the decision rules and the way to average them. Another important issue discussed in this paper is the performance of boosting methods on data which are generated by classes that have a significant overlap, in other words, classification problems in which even the Bayes optimal prediction rule has a significant error. It has been observed by several authors, including those of the current paper, that AdaBoost is not an optimal method in this case. The problem seems to be that AdaBoost overemphasizes the atypical examples which eventually results in inferior rules. In the current",{},{"id":26,"text":2090,"url":26,"identifiers":2091},"Freund (1999).",{},{"id":26,"text":2093,"url":26,"identifiers":2094},"Schapire, R. E., Freund, Y., Bartlett, P. and Lee, W. S. (1998). Boosting the margin: A new explanation for the effectiveness of voting methods. Ann. Statist. 26 1651-1686.",{"doi":2095},"10.1214\u002Faos\u002F1024691352",{"id":26,"text":2097,"url":26,"identifiers":2098},"Bauer, E. and Kohavi, R. (1999). 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MIT Press.",{"doi":2152},"10.7551\u002Fmitpress\u002F3897.001.0001",{"id":26,"text":2154,"url":26,"identifiers":2155},"Mallat, S. and Zhang, Z. (1993). Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Processing 41 3397-3415.",{"doi":1061},{"id":26,"text":2157,"url":26,"identifiers":2158},"Schapire, R. (1997). Using output codes to boost multiclass learning problems. In Proceedings of the Fourteenth International Conference on Machine Learning 313-321. Morgan Kaufman, San Francisco.",{},{"id":26,"text":2160,"url":26,"identifiers":2161},"Schapire, R. E. and Singer, Y. (1998). Improved boosting algorithms using confidence-rated predictions. In Proceedings of the Eleventh Annual Conference on Computational Learning Theory.",{"doi":1072},{"id":26,"text":2163,"url":26,"identifiers":2164},"Schapire, R., Freund, Y., Bartlett, P. and Lee, W. (1998). Boosting the margin: a new explanation for the effectiveness of voting methods. Ann. Statist. 26 1651-1686.",{"doi":2095},{"id":26,"text":2166,"url":26,"identifiers":2167},"Amit, Y. and Geman, D. (1997). Shape quantization and recognition with randomized trees. Neural Comput. 9 1545-1588. Breiman, L. (1999a). Random forests. Technical report, available at www.stat.berkeley.edu. Breiman, L. (1999b). Prediction games and arcing algorithms. Neural Comput. 11 1493-1517.",{"doi":2168},"10.1162\u002Fneco.1997.9.7.1545",{"id":26,"text":2170,"url":26,"identifiers":2171},"Dietterich, T. (2000). An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting and randomization. Mach. Learning 40 139-158.",{"doi":2172},"10.1023\u002FA:1007607513941",{"id":26,"text":2174,"url":26,"identifiers":2175},"B ¨uhlmann, P. and Yu, B. (2000). Explaining bagging. Preprint.",{},{"id":26,"text":2177,"url":26,"identifiers":2178},"Gill, E. P., Murray, W. and Wright, M. H. (1981). Practical Optimization. Academic Press, New York.",{},{"id":26,"text":2180,"url":26,"identifiers":2181},"Breimann, L. (1996). Bagging predictors. Machine Learning 24 123-140.",{"doi":1577},{"id":26,"text":2183,"url":26,"identifiers":2184},"Breiman, L. (1998). Arcing classifiers. Ann. Statist. 26 801-849.",{"doi":2120},{"id":26,"text":2186,"url":26,"identifiers":2187},"Freund, Y. (1999). An adaptive version of the boost by majority algorithm. In Proceedings of the Twelfth Annual Conference on Computational Learning Theory.",{"doi":2188},"10.1145\u002F307400.307419",{"id":26,"text":2190,"url":26,"identifiers":2191},"Freund, Y. and Mason, L. (1999). The alternating decision tree learning algorithm. 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Technical report, Carnegie Mellon Univ.",{},{"id":26,"text":2211,"url":26,"identifiers":2212},"Drucker, H. and Cortes, C. (1996). Boosting decision trees. In Proceedings of Neural Information Processing 8 479-485. MIT Press.",{},{"id":26,"text":2214,"url":26,"identifiers":2215},"Elkan, C. (1997). Boosting and na¨ive Bayes learning. Technical Report CS97-557, Univ. California, San Diego.",{},{"id":26,"text":2217,"url":26,"identifiers":2218},"Hastie, T. and Tibshirani, R. (1998). Bayesian backfitting. Technical report, Stanford Univ.",{},{"id":26,"text":2220,"url":26,"identifiers":2221},"Heikkinen, J. (1998). Curve and surface estimation using dynamic step functions. In Practical Nonparametric and Semiparametric Bayesian Statistics (D. Dey, P. M ¨uller and D. Sinha, eds.) 255-272. Springer, New York.",{"doi":2222},"10.1007\u002F978-1-4612-1732-9_14",{"id":26,"text":2224,"url":26,"identifiers":2225},"Quinlan, J. (1996). Bagging, boosting, and C4.5. In Proceedings Thirteenth American Association for Artificial Intelligence National Conference on Artificial Intelligence 725-730. AAAI Press, Menlo Park, CA.",{},{"id":26,"text":2227,"url":26,"identifiers":2228},"Freund, Y. (1999). An adaptive version of boost by majority algorithm. In Proceedings of the Twelfth Annual Conference on Computational Learning Theory.",{"doi":2188},{"id":26,"text":2230,"url":26,"identifiers":2231},"Friedman, J. H. (1991). Multivariate adaptive regression splines (with discussion). Ann. Statist. 19 1-141. Friedman, J. H. (1999a). Greedy function approximation: a gradient boosting machine. Ann. Statist. To appear. Friedman, J. H. (1999b). Stochastic gradient boosting. Technical report, Dept. Statistics, Stanford Univ.",{},{"id":26,"text":2233,"url":26,"identifiers":2234},"Friedman, J. H. and Hall, P. (1999). On bagging and nonlinear estimation. J. Comput. Graph. Statist. To appear.",{},{"id":26,"text":2236,"url":26,"identifiers":2237},"Grove, A. and Schuurmans, D. (1998). Boosting in the limit: maximizing the margin of learned ensembles. In Proceedings of the Fifteenth National Conference on Artificial Intelligence.",{},{"id":26,"text":2239,"url":26,"identifiers":2240},"Quinlan, J. (1996). Boosting first order learning. In Proceedings of the Seventh International Workshop on Algorithmic Learning Theory (S. Arikawa and A. Sharma, eds.) Lecture Notes in Artificial Intelligence 1160 143-155. Springer, Berlin.",{"doi":2241},"10.1007\u002F3-540-61863-5_42",{"id":26,"text":2243,"url":26,"identifiers":2244},"Ratsch, G. (1998). Ensemble learning methods for classification. Masters thesis, Dept. Computer Science, Univ. Potsdam.",{},{"id":26,"text":2246,"url":26,"identifiers":2247},"Ratsch, G., Onoda, T. and Muller, K. R. (2000). Soft margins for AdaBoost. Machine Learning 1-35.",{},{"id":26,"text":2249,"url":26,"identifiers":2250},"Friedman (1999). 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