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R-Learning

  • 金融分析中求出r/s分析的r/s值

    金融分析中求出r/s分析的r/s值,進(jìn)入spss即可求出hurst值

    標(biāo)簽: 金融

    上傳時(shí)間: 2013-12-13

    上傳用戶:zq70996813

  • 假定已經(jīng)有許多應(yīng)用采用了程序1 - 1 5中所定義的C u r r e n c y類

    假定已經(jīng)有許多應(yīng)用采用了程序1 - 1 5中所定義的C u r r e n c y類,現(xiàn)在我們想要對(duì)C u r r e n c y類 的描述進(jìn)行修改,使其應(yīng)用頻率最高的兩個(gè)函數(shù)A d d和I n c r e m e n t可以運(yùn)行得更快,從而提高應(yīng) 用程序的執(zhí)行速度。由于用戶僅能通過p u b l i c部分所提供的接口與C u r r e n c y類進(jìn)行交互,

    標(biāo)簽: 程序 定義

    上傳時(shí)間: 2015-10-11

    上傳用戶:BIBI

  • 鄰接矩陣類的根是A d j a c e n c y W D i g r a p h

    鄰接矩陣類的根是A d j a c e n c y W D i g r a p h,因此從這個(gè)類開始。程序1 2 - 1給出了類的描述。程 序中,先用程序1 - 1 3中函數(shù)Make2DArray 為二組數(shù)組a 分配空間,然后對(duì)數(shù)組a 初始化,以描述 一個(gè)n 頂點(diǎn)、沒有邊的圖的鄰接矩陣,其復(fù)雜性為( n2 )。該代碼沒有捕獲可能由M a k e 2 D A r r a y 引發(fā)的異常。在析構(gòu)函數(shù)中調(diào)用了程序1 - 1 4中的二維數(shù)組釋放函數(shù)D e l e t e 2 D

    標(biāo)簽: 矩陣

    上傳時(shí)間: 2013-12-21

    上傳用戶:lanjisu111

  • 第二個(gè)搜索函數(shù)為私有成員函數(shù)S a v e S e a r c h

    第二個(gè)搜索函數(shù)為私有成員函數(shù)S a v e S e a r c h,由插入和刪除操作來調(diào)用。S a v e S e a r c h不僅 包含了S e a r c h的功能,而且可把每一級(jí)中遇到的最后一個(gè)節(jié)點(diǎn)存放在數(shù)組l a s t之中

    標(biāo)簽: 函數(shù) 搜索

    上傳時(shí)間: 2015-10-11

    上傳用戶:zm7516678

  • ertfv ertt vweretv werf r

    ertfv ertt vweretv werf r

    標(biāo)簽: vweretv ertfv ertt werf

    上傳時(shí)間: 2015-10-15

    上傳用戶:wanqunsheng

  • egdr grg g r dgr drg dgdr gdr gzdrg

    egdr grg g r dgr drg dgdr gdr gzdrg

    標(biāo)簽: gzdrg egdr dgdr dgr

    上傳時(shí)間: 2015-10-19

    上傳用戶:alan-ee

  • 本程序是D.R.J.OWEN主編的FINITE ELEMENTS IN PLASTICITYG一書中第八章例題的有限元程序

    本程序是D.R.J.OWEN主編的FINITE ELEMENTS IN PLASTICITYG一書中第八章例題的有限元程序,網(wǎng)上流傳的這個(gè)版本的源碼里有錯(cuò)誤,我把它更正過來了,而且還添加了輸入文本。絕對(duì)好東西啊。

    標(biāo)簽: PLASTICITYG ELEMENTS FINITE OWEN

    上傳時(shí)間: 2014-01-23

    上傳用戶:myworkpost

  • D.R.J.OWEN的經(jīng)典著作--< 有限元程序>

    D.R.J.OWEN的經(jīng)典著作--< 有限元程序>

    標(biāo)簽: OWEN lt gt 有限元

    上傳時(shí)間: 2013-12-25

    上傳用戶:dave520l

  • Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general

    Learning Kernel Classifiers: Theory and Algorithms, Introduction This chapter introduces the general problem of machine learning and how it relates to statistical inference. 1.1 The Learning Problem and (Statistical) Inference It was only a few years after the introduction of the first computer that one of man’s greatest dreams seemed to be realizable—artificial intelligence. Bearing in mind that in the early days the most powerful computers had much less computational power than a cell phone today, it comes as no surprise that much theoretical research on the potential of machines’ capabilities to learn took place at this time. This becomes a computational problem as soon as the dataset gets larger than a few hundred examples.

    標(biāo)簽: Introduction Classifiers Algorithms introduces

    上傳時(shí)間: 2015-10-20

    上傳用戶:aeiouetla

  • In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-dat

    In 1960, R.E. Kalman published his famous paper describing a recursive solution to the discrete-data linear filtering problem. Since that time, due in large part to advances in digital computing, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation.

    標(biāo)簽: R.E. discrete-dat describing published

    上傳時(shí)間: 2015-10-22

    上傳用戶:2404

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