476.  迴歸分析入門：SAS程式報表精析解讀與個案建模範例

林志娟; 林志鴻; 張慶暉 ,  2009
[Graduate Institute & Department of Statistics] Monograph
初版
~J..!0f線!8- -~性"~" 'I迴~i ~'歸@ Je分.t;..P.. 析/1']- 1(.J".,':;I. 一(_) _一/原1;r6, )J始(.0b I~模f:•'/'(•: 型~:±IJ: 與u/l,l假1fp.~ll設-ot1x1. 3-1 3.J i

477.  近代臺灣航空與軍需產業的發展及技術轉型(1920s-1960s)

曾令毅 ,  2018
[Graduate Institute & Department of History] Monograph
品-8、' ~高aI雄*f工aI業1f控fi手',普&j及iij海~m軍ffj飛:~行ji場ijJ海¥j岸iij海EO部tJi填ji海ij撞~fi地Eg計tl!觀J1園I[(1934 )--一--…------1-121 [僵I] 3-9、' 1932 年uf. 5F月l ~高u1雄HI糾'I知

478.  Inverse spectral problems for non-selfadjoint second-order differential operators with Dirichlet boundary conditions

Buterin, Sergey A; Shieh, Chung-Tsun; Yurko, Vjacheslav A ,  2013-12-01
[Department of Applied Mathematics and Data Science] Journal Article
Boundary Value Problems 2013, 2013:180 (24pages)
. Lemma  (i) For |ρ|→∞, the folluwing asymptotics holds ⎫ sinρx cosρx  x S(x,λ)= – Q(x

479.  核多邊形圖的密度函數估計與適合度檢定

伍志祥 ,  2007
[Department of Applied Mathematics and Data Science] Research Paper
淡江大學數學學系 伍志祥 核多邊形圖的密度函數估計與適合度檢定 Kernel Polygons for Density Function Estimation and Testing of Goodness of Fit Edge frequence polygon;Frequence

480.  資料探勘理論與應用：以IBM SPSS Modeler為範例

廖述賢; 溫志皓 ,  2012-01
[Department of Management Sciences] Monograph
DrMaster；初版
~ .......................................................................4-主2 4-2 IBM SPSS Modeeler 資~*料~格m式~及&設~定~ ........ ............................4-占5 4-3 自El動i1資J~料*~準~i備I

481.  A note on kernel polygons

林千代; Lin, Chien-tai; 伍志祥; Wu, Jyh-shyang; 顏嘉宏; Yen Chia-hung ,  2006-03
[Department of Applied Mathematics and Data Science] Journal Article
Biometrika 93(1), pp.228-234
. 1. I Let X ...,X be a univariate random sample from a continuous distribution with density 1 n function f

482.  『色彩を持たない多崎つくると、彼の巡礼の年』におけるメディウム ―「色彩を持たない」多崎つくると木元沙羅をめぐって―

廖育卿 ,  2015-08
[Graduate Institute & Department of Japanese] Chapter
村上春樹におけるメディアム 21世紀篇 頁123-147
-) *~摘.f要-: 如$19 1讀.:村ff上J:.春.*i樹jt的a~作11品tfo內pq容~，, 常'f常'f可ar以~).看;t到J1作11者*:對ft於~人A.'性11.深.~入A的fA錯~~盒:t，'萃3E的51 起~ 請~者*共# 疇~~。0 -村# J上:.春.*樹~t

483.  具有高色差對比之(3, n)-門檻式視覺密碼學

官振宇; 侯永昌; 蔡志豐 ,  2014-11-01
[Graduate Institute & Department of Information Management] Journal Article
中正嶺學報 43(2)，頁141-155
極佳的視覺效果。相較於(3, n)-視覺分享機制的相關研究，本研究具備下列幾項特點：(1)設計概念十分單純，易於實作。(2)以「在n個位置中任選n–n0.5+1個位置來填入1值」的方式來設計視覺密碼學所需之分享矩陣的內容，可以在重疊任意3張分享投影片時出現最佳的黑白色差，並且在還原影像上產生極佳的視

484.  Assessing Ordinal Logistic Regression Models via Nonparametric Smoothing

Lin, K. C.; 陳怡如; Chen, Yi-ju ,  2008-01-01
[Graduate Institute & Department of Statistics] Journal Article
Communications in Statistics : Theory and Methods 37(6) , pp. 917-930
淡江大學統計學系 Lin, K. C. 陳怡如 Chen, Yi-ju Assessing Ordinal Logistic Regression Models via Nonparametric Smoothing Taylor & Francis Cumulative logit model

485.  Nonstationalry spatial modeling and application

張雅梅; Chang, Ya-mei ,  2008
[Graduate Institute & Department of Statistics] Thesis
:t條f't1件9自m迺j!~歸H*模~~型~ (condi­ tional autoregressive model) (Besag, 1974),， 統~ft.計-Jt推.:t論!ii如kY-|'H採*r用n J貝H\氏~分{J析Ti法t (Biyesian inference)o。i這i個

486.  多重代理人系統應用於輕軌列車運行調度模擬之研究

陶治中; 江品瑩 ,  2014-06-30
[Graduate Institute & Department of Transportation Management] Journal Article
運輸計劃季刊 43(2), p.173-203
•~定別m督~•報為~管.*軌:~.理車a封~*範E輛車*_關O為運~.內~鋼行M~之~輪.控~代R鋼M制~理.軌~、·人A系J判~與~ •統.，以~GP~S~為*車.輪~定d位J系.撓·，並I具A備.車*議•通•訊•設t備.與~軌~旁*定~位~設t施A.·~9'J*車 現.行~期m為~移$動_開M憲

487.  Mining marketing knowledge to explore social network sites and online purchase behaviors

Liao, Shu-Hsien; Hsiao, Pei-Yuan; Li, Chien-Wen; Lin, Yun-Fei ,  2015-07-24
[Department of Management Sciences] Journal Article
Applied Artificial Intelligence 29(7), pp.679-732
as a reference for decision making. The association rules are defined as follows (Wang et al. 2004): Make I ={i1, i2, ...im} the item set, in which each item

488.  關於Hadamard不等式一些更細緻的結果

古皓彰; Ku, Hao-Chang ,  2015
[Department of Applied Mathematics and Data Science] Thesis
;Hermite-Hadamard’s inequality;Convex function 設f:Ⅰ→R是一個定義在區間Ⅰ上的凸函數，a,b∈Ⅰ ai=1)^n▒〖λ_i∙x_i 〗)≤∑_(i=1)^n▒〖λ_i∙f(x_i)〗對於所有 x_1,x_2,…x_n∈I 且 λ_1,λ_2