使用到的參數(shù)跟談到彈性網(wǎng)絡(luò)的那一章里頭所講的是一樣的， ke 則是終止條件。如果 step 被打勾，則程式在每一步之間會(huì)暫停 100毫秒（或其他使用者輸入的數(shù)值）。如果 Random 被打勾，則程式會(huì)以系統(tǒng)時(shí)間作為亂數(shù)產(chǎn)生器的種子數(shù)，否則，就以使用者輸入的數(shù)（ Random 右邊那一格）為種子數(shù)。
你可以利用 load 來(lái)載入推銷員問(wèn)題檔與其最佳解，如此便可比較彈性網(wǎng)絡(luò)所找出來(lái)的解與最佳解差了多少。
Central, Radius, and Error 這三個(gè)參數(shù)的前兩個(gè)，只影響彈性網(wǎng)絡(luò)的起使位置和大小，對(duì)求解沒(méi)有影響。第三個(gè)參數(shù)代表城市與網(wǎng)絡(luò)點(diǎn)之間的容忍距離，也就是說(shuō)，如果某城市與某網(wǎng)絡(luò)點(diǎn)之間的距離，小于容忍距離，那就把這個(gè)城市當(dāng)成是被該網(wǎng)絡(luò)點(diǎn)所拜訪。
按下小 w按鈕會(huì)將目前的結(jié)果與參數(shù)值寫(xiě)到“en.out”這個(gè)檔案。使得我們可以很方便地來(lái)比較不同參數(shù)的效果。
xl2tpd a Layer 2 Tunneling Protocol (L2TP) daemon. It supports IPsec SA reference tracking, which enables the IPsec stacks to support multiple l2tp clients behind the same NAT router and multiple l2tp clients on the same internal IP address. It is a fork of "l2tpd".
Release focus: Minor feature enhancements
Changes:
Support for passwordfd, a workaround for some Cisco routers, and extended logging.
siptapi
A TAPI driver for SIP. With this TAPI driver you have a click2dial feature with any TAPI enabled application (e.g. MS Outlook) and any SIP account (e.g. freeworlddialup or iptel.org).
The present document specifies the CAMEL Application Part (CAP) supporting the fourth phase of the network feature Customized Applications for Mobile network enhanced Logic. CAP is based on a sub-set of the ETSI Core INAP CS-2 as specified by ETSI en 301 140 1 [26]. Descriptions and definitions provided by ETSI en 301 140 1 [26] are directly referenced by this standard in the case no additions or clarifications are needed for the use in the CAP.
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
