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combntns函數(shù)所引起的內(nèi)存不足問題

編寫NFL和NFP分類器應(yīng)用于ASLAN數(shù)據(jù)庫(kù)發(fā)現(xiàn)combntns函數(shù)所引起的內(nèi)存不足，到網(wǎng)上搜索有人提問，但沒有解決方案。
問題：我想取一個(gè)序列的任意組合，用combntns，在數(shù)目較小時(shí)，可以達(dá)到目的，但是數(shù)目變大時(shí)，就不行了，求助各位。

例如取1:5,里面3個(gè)數(shù)字的組合，很容易得到。
combntns(1:5,3) 就可以了

但是取1:100里面10個(gè)數(shù)字的組合，就不行了。
我的解決方案：如果你未必要取所有組合，可以從1：100中任取20個(gè)數(shù)，再取這20個(gè)數(shù)字里面10個(gè)數(shù)字的組合。
%matlab code:

rand('state',0);

temp1 = randperm(100);

temp2 = temp1(1:20);

combntns(temp2,10)

posted @ 2012-12-13 08:30 杰哥閱讀(826) | 評(píng)論 (0) | 編輯收藏

win7下matlab7.0的卸載問題,為什么matlab2010a只能在斷網(wǎng)時(shí)才能打開？

http://www.ilovematlab.cn/thread-68860-1-1.html

在win7，32位家庭版下,有兩種方法解決正常運(yùn)行問題，一是將配色方案改為經(jīng)典(在桌面空白處右擊鼠標(biāo)，選擇個(gè)性化，在彈出的對(duì)話框中選擇windows經(jīng)典主題即可)，二是兼容性改為vista sp1或sp2.卸載完了后，再把主題改回到你原來的主題。

為什么matlab2010a只能在斷網(wǎng)時(shí)才能打開？
20121207發(fā)現(xiàn)matlab打不開，上網(wǎng)搜索解決方案。有人說斷網(wǎng)就可以。我實(shí)驗(yàn)兩次，注銷后斷網(wǎng)能打開matlab，注銷后連網(wǎng)，不打開其他任何程序，matlab還是打不開，等了五分鐘。能看到matlab進(jìn)程。繼續(xù)搜索“Windows 7 matlab斷網(wǎng)才能打開”。查到解決方案(http://wenwen.soso.com/z/q294348187.htm)：你可能用的是window7吧，這種現(xiàn)象的產(chǎn)生是由于MatLab與Windows 7 的兼容性問題，解決的方法如下：從純凈的Windows XP系統(tǒng)中system32目錄中拷貝一份iphlpapi.dll到 Matlab 2010a安裝目錄\bin\win32中問題即可解決，當(dāng)然，X64的系統(tǒng)要到 Windows XP X64 中去拷。我問Lianyang Ma要了這個(gè)文件，重啟下電腦就搞好了(即使在同時(shí)打開qq,gtalk,谷歌瀏覽器,FileZilla的情況下，也能打開matlab)。與配色方案和兼容性沒有關(guān)系。

posted @ 2012-12-05 19:32 杰哥閱讀(1450) | 評(píng)論 (0) | 編輯收藏

全國(guó)接聽免費(fèi)附加套餐

http://service.ah.10086.cn/pub-page/busi/commonBusi/optpage_common.html?f=200011716&tfCode=JIETINGF&kind=200011524&area=cd

http://www.10086.cn/ah/，網(wǎng)上營(yíng)業(yè)廳，不是點(diǎn)擊“業(yè)務(wù)辦理”，而是點(diǎn)擊“換套餐”，長(zhǎng)途漫游類，全國(guó)接聽免費(fèi)附加套餐

posted @ 2012-11-23 17:46 杰哥閱讀(1484) | 評(píng)論 (5) | 編輯收藏

arXiv

地球上首屈一指的科學(xué)論文預(yù)印本網(wǎng)站，科學(xué)出版業(yè)開放獲取運(yùn)動(dòng)的領(lǐng)頭羊，目前主站點(diǎn)落腳于康乃爾大學(xué)，在世界各地設(shè)有17個(gè)鏡像站點(diǎn)，中國(guó)的鏡像站點(diǎn)是 http://cn.arXiv.org 。

現(xiàn)今的數(shù)學(xué)家及科學(xué)家習(xí)慣將論文先上傳至 arXiv.org ，再提交給專業(yè)的學(xué)術(shù)期刊。盡管 arXiv 上的文章未經(jīng)同行評(píng)審，2004年起采行了一套“認(rèn)可”系統(tǒng)。

arXiv.org的創(chuàng)始人物理學(xué)家Paul Ginsparg因之獲得了2002年的麥克阿瑟獎(jiǎng)。在其上能搜索Professor Tao的姓名就可以搜索到其論文。

posted @ 2012-11-23 16:31 杰哥閱讀(2219) | 評(píng)論 (0) | 編輯收藏

Multiple kernel learning (MKL)

看論文SimpleMKL第一節(jié)標(biāo)注部分就能完全掌握其概念

posted @ 2012-11-22 18:27 杰哥閱讀(1086) | 評(píng)論 (0) | 編輯收藏

Subgradient method

http://en.wikipedia.org/wiki/Subgradient_method
Classical subgradient rules

Let $f:\mathbb{R}^n \to \mathbb{R}$ be a convex function with domain $\mathbb{R}^n$ . A classical subgradient method iterates

$x^{(k+1)} = x^{(k)} - \alpha_k g^{(k)} \$

where $g^{(k)}$ denotes a subgradient of $f \$ at $x^{(k)} \$ . If $f \$ is differentiable, then its only subgradient is the gradient vector $\nabla f$ itself. It may happen that $-g^{(k)}$ is not a descent direction for $f \$ at $x^{(k)}$ . We therefore maintain a list $f_{\rm{best}} \$ that keeps track of the lowest objective function value found so far, i.e.

$f_{\rm{best}}^{(k)} = \min\{f_{\rm{best}}^{(k-1)} , f(x^{(k)}) \}.$
下圖來自: http://www.stanford.edu/class/ee364b/notes/subgradients_notes.pdf
例2：SVM代價(jià)函數(shù)是hinge loss，在(1,0)除導(dǎo)數(shù)不存在，取1和1之間的數(shù)值，具體怎么取？Mingming Gong said好像這個(gè)pdf和http://www.stanford.edu/class/ee364b/lectures/subgrad_method_slides.pdf，其中一個(gè)講了。Mingming Gong asked tianyi, which is better, subgradient or smooth apprpximation?結(jié)論是不一定，subgradient解的是原問題，smooth不是解的原問題。一個(gè)相當(dāng)于對(duì)梯度的近似，一個(gè)是對(duì)函數(shù)的近似，很難說哪個(gè)好。

posted @ 2012-11-22 16:08 杰哥閱讀(845) | 評(píng)論 (0) | 編輯收藏

How to use matlab solve optimization quadratic?

Nannan gives me a fold named "Matlab Help". On page 46 of "Optimization Toolbox User Guide", it lists the constrain and objective type, and the matlab function. For example, if the constrain is linear and the objective is quadratic, we can use quadprog. Note that it can not slove $D_1$ in Section 4.1 of "Smooth minimization of non-smooth functions". Problem: max ((X^T)HX) and H is positive semi definite. The matlab function "quadratic" can not solve this kind of problem. It can only solve the problem: min ((X^T)HX) and H is positive semi definite.

posted @ 2012-11-21 18:31 杰哥閱讀(682) | 評(píng)論 (0) | 編輯收藏

Cauchy–Schwarz inequality

http://en.wikipedia.org/wiki/Cauchy%E2%80%93Schwarz_inequality

The Cauchy–Schwarz inequality states that for all vectors x and y of an inner product space it is true that

$|\langle x,y\rangle| ^2 \leq \langle x,x\rangle \cdot \langle y,y\rangle,$

where $\langle\cdot,\cdot\rangle$ is the inner product. Equivalently, by taking the square root of both sides, and referring to the norms of the vectors, the inequality is written as

$|\langle x,y\rangle| \leq \|x\| \cdot \|y\|.\,$

Notable special cases

[edit]Rⁿ

In Euclidean space Rⁿ with the standard inner product, the Cauchy–Schwarz inequality is

$\left(\sum_{i=1}^n x_i y_i\right)^2\leq \left(\sum_{i=1}^n x_i^2\right) \left(\sum_{i=1}^n y_i^2\right).$

posted @ 2012-11-21 11:03 杰哥閱讀(497) | 評(píng)論 (0) | 編輯收藏

Lipschitz continuity

Given two metric spaces (X, d_X) and (Y, d_Y), where d_X denotes the metric on the set X and d_Y is the metric on set Y (for example, Y might be the set of real numbers R with the metric d_Y(x, y) = |x − y|, and X might be a subset of R), a function

$\displaystyle f: X \to Y$

is called Lipschitz continuous if there exists a real constant K ≥ 0 such that, for all x₁ and x₂ in X,

$d_Y(f(x_1), f(x_2)) \le K d_X(x_1, x_2).$ ^[1]

Any such K is referred to as a Lipschitz constant for the function ƒ.
Reference：http://en.wikipedia.org/wiki/Lipschitz_continuity
區(qū)分概念，Lipschitz Continuously Differentiable，見Nesterov’s Optimal Gradient Method(Yaoliang Yu的ppt第十頁(yè))

[zz] 關(guān)于Lipschitz連續(xù)性的幾個(gè)例子 http://blog.sina.com.cn/s/blog_544a70700100fqix.html
1，f(x) = |x|

是Lipschitz連續(xù)的，Lipschitz常數(shù)為1，但是其不可微

2，f(x) = x²

是Lipschitz不連續(xù)的，在無(wú)窮大處，f'(x)=2x,趨于無(wú)窮陡

3，f(x) = √x defined on [0, 1] is not Lipschitz continuous. This function becomes infinitely steep as x approaches 0 since its derivative becomes infinite.

4,The function f(x) = x^3/2sin(1/x) (x ≠ 0) and f(0) = 0, restricted on [0, 1], gives an example of a function that is differentiable on a compact set while not locally Lipschitz because its derivative function is not bounded.

f'(x)=3/2√x sin(1/x)-cos(1/x)/√x

趨向于0的時(shí)候無(wú)界

posted @ 2012-11-20 10:42 杰哥閱讀(1426) | 評(píng)論 (0) | 編輯收藏

The difference between Fisher’s Linear Discriminant and Linear Discriminant Analysis

Fisher’s Linear Discriminant is for classification while Linear Discriminant Analysis is for dimension reduction. How is Fisher’s Linear Discriminant used for classification? The idea is very simple. It first reduces the dimension to one dimensional space using Linear Discriminant Analysis and then choose average of the two projected means as a threshold for classification. Therefore, the code is very simple to implement. See pages 13-18 of the slides "Linear Classification" of my computer for details.
The differences between them have been mentioned by Professor Tao.

Other reference: "Introduction to Machine Learning-dietterich" of my computer

posted @ 2012-11-14 12:05 杰哥閱讀(600) | 評(píng)論 (3) | 編輯收藏

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閱讀排行榜

評(píng)論排行榜

http://en.wikipedia.org/wiki/Subgradient_method
Classical subgradient rules

Notable special cases

[edit]Rⁿ

青青草原综合久久大伊人导航_色综合久久天天综合_日日噜噜夜夜狠狠久久丁香五月_热久久这里只有精品

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留言簿(57)

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Other

Paper submission

福彩

留學(xué)相關(guān)

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中科大和中科院

搜索

最新評(píng)論

閱讀排行榜

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http://en.wikipedia.org/wiki/Subgradient_methodClassical subgradient rules

Notable special cases

[edit]Rn

http://en.wikipedia.org/wiki/Subgradient_method
Classical subgradient rules

[edit]Rⁿ