This is balance and fall article. It will be helpful for motor behavior and rehabilitation study.
標(biāo)簽： rehabilitation and behavior balance
上傳時(shí)間： 2017-07-18
上傳用戶：xuanjie
This is balance and fall article. It will be helpful for motor behavior and rehabilitation study.
標(biāo)簽： rehabilitation and behavior balance
上傳時(shí)間： 2017-07-19
上傳用戶：cuiyashuo
this is 3d tr model for airplane target tracking
標(biāo)簽： airplane tracking target model
上傳時(shí)間： 2017-07-20
上傳用戶：shinesyh
LinQ SQL TẤ N CÔ NG KIỂ U SQL INJECTION - TÁ C HẠ I VÀ PHÒ NG TRÁ NH
標(biāo)簽： INJECTION SQL LinQ 7844
上傳時(shí)間： 2013-12-15
上傳用戶：eclipse
bi directional anchored/headed lists libraray
標(biāo)簽： directional anchored libraray headed
上傳時(shí)間： 2013-12-25
上傳用戶：xcy122677
Fortran - Tóm tắ t nộ i dung mô n họ c Các khái niệ m và yế u tố trong ngô n ngữ lậ p trình FORTRAN. Các câ u lệ nh củ a ngô n ngữ FORTRAN. Cơ bả n về chư ơ ng chư ơ ng dị ch và mô i trư ờ ng lậ p trình DIGITAL Visual Fortran. Viế t và chạ y các chư ơ ng trình cho các bài toán đ ơ n giả n bằ ng ngô n ngữ FORTRAN.
標(biāo)簽： Fortran 7855 7897 7885
上傳時(shí)間： 2013-12-25
上傳用戶：songrui
metricmatlab ch ¬ ng 4 Ma trË n - c¸ c phÐ p to¸ n vÒ ma trË n. 4.1 Kh¸ i niÖ m: - Trong MATLAB d÷ liÖ u ® Ó ® a vµ o xö lý d íi d¹ ng ma trË n. - Ma trË n A cã n hµ ng, m cét ® î c gä i lµ ma trË n cì n m. § î c ký hiÖ u An m - PhÇ n tö aij cñ a ma trË n An m lµ phÇ n tö n» m ë hµ ng thø i, cét j . - Ma trË n ® ¬ n ( sè ® ¬ n lÎ ) lµ ma trË n 1 hµ ng 1 cét. - Ma trË n hµ ng ( 1 m ) sè liÖ u ® î c bè trÝ trª n mét hµ ng. a11 a12 a13 ... a1m - Ma trË n cét ( n 1) sè liÖ u ® î c bè trÝ trª n 1 cét.
標(biāo)簽： metricmatlab 203 184 tr
上傳時(shí)間： 2017-07-29
上傳用戶：來茴
根據(jù)ns的執(zhí)行結(jié)果程序out.tr測(cè)延遲時(shí)間
上傳時(shí)間： 2017-07-31
上傳用戶：hewenzhi
Harmonic Balance Simulation on ADS
標(biāo)簽： Simulation Harmonic Balance ADS
上傳時(shí)間： 2014-01-16
上傳用戶：bcjtao
In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data classification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local scaling heuristic strategy, then optimize a pair of nonparallel hyperplanes through two smaller sized support vector machine (SVM)-typed problems. In the prediction stage, BDTWSVMs assign to the class label depending on the kernel density degree-based distances from each test point to the two hyperplanes. BDTWSVMs not only inherit good properties from twin support vector machines (TWSVMs) but also give good description for data points. The experimental results on toy as well as publicly available datasets indicate that BDTWSVMs compare favorably with classical SVMs and TWSVMs in terms of generalization
標(biāo)簽： recognition Bi-density machines support pattern vector twin for
上傳時(shí)間： 2019-06-09
上傳用戶：lyaiqing
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