Short description: GUI Ant-Miner is a tool for extracting classification rules from data. It is an updated version of a data mining algorithm called Ant-Miner (Ant Colony-based Data Miner), which was proposed in 2002 by Parpinelli, Lopes and Freitas. GUI Ant-Miner differs from the original algorithm as follows: It has a friendly graphical user interface, makes possible the use of ant populations within the Ant Colony OPTIMIZATION (ACO) concept, data input file is standardized with the well-known Weka system, and runs on virtually any operating system since it is written in Java.
標(biāo)簽： classification description extracting Ant-Miner
上傳時間： 2013-12-18
上傳用戶：gonuiln
genetic algorithm (or GA) is a search technique used in computing to find true or approximate solutions to OPTIMIZATION and search problems for function of 2 variable
標(biāo)簽： approximate algorithm computing technique
上傳時間： 2017-07-25
上傳用戶：225588
Click is a modular router toolkit. To use it you ll need to know how to compile and install the software, how to write router configurations, and how to write new elements. Our ACM Transactions on Computer Systems paper, available from the Web site, will give you a feeling for what Click can do. Using the OPTIMIZATION tools under CLICKDIR/tools, you can get even better performance than that paper describes.
標(biāo)簽： compile install modular toolkit
上傳時間： 2013-12-20
上傳用戶：wangchong
單個網(wǎng)頁的最優(yōu)化時搜索引擎優(yōu)化的(seo)的細致工作，需要一頁頁的展開，所以，這個工作也是讓人感到郁悶和不耐煩的事情，特別是要優(yōu)化很多頁面時，那種心情更是…….. 可是即時非常的郁悶，但，優(yōu)化工作，每一頁的優(yōu)化都馬虎不得，它直接關(guān)系到搜索引擎排名是否如意出現(xiàn)，因此，開始這個工作之前，是需要毅力和耐力的。 網(wǎng)站的優(yōu)化也被稱為頁面優(yōu)化（on-page OPTIMIZATION），就是通過改進頁面的修飾性的因素，如標(biāo)題、描述、題頭文字等等，就是盡可能的當(dāng)搜索引擎訪問網(wǎng)站時，能讓它迅速的抓住網(wǎng)頁的要領(lǐng)，完整的將網(wǎng)頁所發(fā)布的信息帶走，而這些改進，都是圍繞著關(guān)鍵詞的使用來進行的。
標(biāo)簽： SEO
上傳時間： 2015-03-01
上傳用戶：whf 13993638362
Convex OPTIMIZATION problem
標(biāo)簽： Convex OPTIMIZATION
上傳時間： 2015-03-23
上傳用戶：飛來大貨車
為工程優(yōu)化技術(shù)的優(yōu)秀代表，Isight 5.5軟件由法國Dassault/Simulia公司出品，能夠幫助設(shè)計人員、仿真人員完成從簡單的零部件參數(shù)分析到復(fù)雜系統(tǒng)多學(xué)科設(shè)計優(yōu)化（MDO, Multi-Disciplinary Design OPTIMIZATION）工作。Isight將四大數(shù)學(xué)算法（試驗設(shè)計、近似建模、探索優(yōu)化和質(zhì)量設(shè)計）融為有機整體，能夠讓計算機自動化、智能化地驅(qū)動數(shù)字樣機的設(shè)計過程，更快、更好、更省地實現(xiàn)產(chǎn)品設(shè)計。毫無疑問，以Isight為代表的優(yōu)化技術(shù)必將為中國經(jīng)濟從“中國制造”到“中國創(chuàng)造”的轉(zhuǎn)型做出應(yīng)有的貢獻！
標(biāo)簽： isight參數(shù)優(yōu)化理論 實例
上傳時間： 2016-02-27
上傳用戶：SteveWang0821
Computational models are commonly used in engineering design and scientific discovery activities for simulating complex physical systems in disciplines such as fluid mechanics, structural dynamics, heat transfer, nonlinear structural mechanics, shock physics, and many others. These simulators can be an enormous aid to engineers who want to develop an understanding and/or predictive capability for complex behaviors typically observed in the corresponding physical systems. Simulators often serve as virtual prototypes, where a set of predefined system parameters, such as size or location dimensions and material properties, are adjusted to improve the performance of a system, as defined by one or more system performance objectives. Such OPTIMIZATION or tuning of the virtual prototype requires executing the simulator, evaluating performance objective(s), and adjusting the system parameters in an iterative, automated, and directed way. System performance objectives can be formulated, for example, to minimize weight, cost, or defects; to limit a critical temperature, stress, or vibration response; or to maximize performance, reliability, throughput, agility, or design robustness. In addition, one would often like to design computer experiments, run parameter studies, or perform uncertainty quantification (UQ). These approaches reveal how system performance changes as a design or uncertain input variable changes. Sampling methods are often used in uncertainty quantification to calculate a distribution on system performance measures, and to understand which uncertain inputs contribute most to the variance of the outputs. A primary goal for Dakota development is to provide engineers and other disciplinary scientists with a systematic and rapid means to obtain improved or optimal designs or understand sensitivity or uncertainty using simulationbased models. These capabilities generally lead to improved designs and system performance in earlier design stages, alleviating dependence on physical prototypes and testing, shortening design cycles, and reducing product development costs. In addition to providing this practical environment for answering system performance questions, the Dakota toolkit provides an extensible platform for the research and rapid prototyping of customized methods and meta-algorithms
標(biāo)簽： OPTIMIZATION and Uncertainty Quantification
上傳時間： 2016-04-08
上傳用戶：huhu123456
evolution computing 現(xiàn)在最火的一篇論文 Handling Multiple Objectives With Particle Swarm OPTIMIZATION
上傳時間： 2016-07-01
上傳用戶：白水煮瓜子
蟻群算法（ant colony OPTIMIZATION, ACO），又稱螞蟻算法，是一種用來在圖中尋找優(yōu)化路徑的機率型算法。
上傳時間： 2016-08-21
上傳用戶：steveng
We consider the problem of target localization by a network of passive sensors. When an unknown target emits an acoustic or a radio signal, its position can be localized with multiple sensors using the time difference of arrival (TDOA) information. In this paper, we consider the maximum likelihood formulation of this target localization problem and provide efficient convex relaxations for this nonconvex OPTIMIZATION problem.We also propose a formulation for robust target localization in the presence of sensor location errors. Two Cramer-Rao bounds are derived corresponding to situations with and without sensor node location errors. Simulation results confirm the efficiency and superior performance of the convex relaxation approach as compared to the existing least squares based approach when large sensor node location errors are present.
標(biāo)簽： 傳感器網(wǎng)絡(luò)
上傳時間： 2016-11-27
上傳用戶：xxmluo
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