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Description: Objective_C 入门的经典教材,详细介绍了Objective_C 的各个细节-Getting Started Objective_C classic materials, described in detail the various details Objective_C
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Size: 887808 |
Author: 王钧 |
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Description: 学习Objective-C的宝典，O Reilly出版的。-Learning Objective-C of the book, O Reilly published.
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Size: 5254144 |
Author: frene |
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Description: 苹果官网指定的学习object-c的书籍-Apple s official website designated learning object-c books
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Size: 876544 |
Author: dadalan |
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Description: iphone学习教材
iphone学习教材-iphone study
iphone study
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Size: 7262208 |
Author: gilay |
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Description: This basic book when you learn objective-c.
This content is translated Korean.
Original is http://cocoadevcentral.com/articles/000081.php.-This is basic book when you learn objective-c.
This content is translated Korean.
Original is http://cocoadevcentral.com/articles/000081.php.
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Size: 624640 |
Author: LeeJaewhan |
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Description: MATLAB函数参考手册，查看matlab函数作用以及功能。- SVMLSPex02.m
Two Dimension SVM Problem, Two Class and Separable Situation
Difference with SVMLSPex01.m:
Take the Largrange Function (16)as object function insteads ||W||,
so it need more time than SVMLSex01.m
Method from Christopher J. C. Burges:
"A Tutorial on Support Vector Machines for Pattern Recognition", page 9
Objective: min "f(A)=-sum(ai)+sum[sum(ai*yi*xi*aj*yj*xj)]/2" ,function (16)
Subject to: sum{ai*yi}=0 ,function (15)
and ai>=0 for any i, the particular set of constraints C2 (page 9, line14).
The optimizing variables is "Lagrange Multipliers": A=[a1,a2,...,am],m is the number of total samples.
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Size: 561152 |
Author: 王东东 |
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Description: objective-c 2.0 入门学习-begin programming
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Size: 918528 |
Author: 王定方 |
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Description: Objective-C2.0程序设计(原书第2版)高清完整版-Objective- C2.0 programming (original book version 2) hd the full version
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Size: 64366592 |
Author: sudd |
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Description: 经典粒子群算法，经过多次优化，演示出图， 待优化的目标函数：N
粒子数目：N
惯性权重：w
学习因子：c1,c2
最大迭代次数：M
问题的维数：D
目标函数取最小值时自变量值：xm
目标函数的最小值：fv-Classical particle swarm algorithm, optimized for many times, demonstrates plotting objective function to be optimized: N of the number of particles: N inertia weight weight: w learning factor: c1, c2 maximum number of iterations: M dimension independent variables: D objective function is to take the minimum value: xm minimum value of the objective function: fv
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Size: 1024 |
Author: 杜青青 |
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