a Java toolkit for training, testing, and applying Bayesian Network Classifiers. Implemented classifiers have been shown to perform well in a variety of artificial intelligence, machine learning, and data mining applications.
標(biāo)簽： Classifiers Implemented Bayesian applying
上傳時(shí)間： 2015-09-11
上傳用戶：ommshaggar
上下文無(wú)關(guān)文法（Context-Free Grammar, CFG）是一個(gè)4元組G=(V, T, S, P)，其中，V和T是不相交的有限集，S∈V，P是一組有限的產(chǎn)生式規(guī)則集，形如A→α，其中A∈V，且α∈(V∪T)*。V的元素稱為非終結(jié)符，T的元素稱為終結(jié)符，S是一個(gè)特殊的非終結(jié)符，稱為文法開始符。 設(shè)G=(V, T, S, P)是一個(gè)CFG，則G產(chǎn)生的語(yǔ)言是所有可由G產(chǎn)生的字符串組成的集合，即L(G)={x∈T* | Sx}。一個(gè)語(yǔ)言L是上下文無(wú)關(guān)語(yǔ)言（Context-Free Language, CFL），當(dāng)且僅當(dāng)存在一個(gè)CFG G，使得L=L(G)。 *⇒ 例如，設(shè)文法G：S→AB A→aA|a B→bB|b 則L(G)={a^nb^m | n,m>=1} 其中非終結(jié)符都是大寫字母，開始符都是S，終結(jié)符都是小寫字母。
標(biāo)簽： Context-Free Grammar CFG
上傳時(shí)間： 2013-12-10
上傳用戶：gaojiao1999
一:需求分析 1. 問(wèn)題描述 魔王總是使用自己的一種非常精練而抽象的語(yǔ)言講話,沒(méi)人能聽懂,但他的語(yǔ)言是可逐步解釋成人能聽懂的語(yǔ)言,因?yàn)樗恼Z(yǔ)言是由以下兩種形式的規(guī)則由人的語(yǔ)言逐步抽象上去的: ----------------------------------------------------------- (1) a---> (B1)(B2)....(Bm) (2)[(op1)(p2)...(pn)]---->[o(pn)][o(p(n-1))].....[o(p1)o] ----------------------------------------------------------- 在這兩種形式中,從左到右均表示解釋.試寫一個(gè)魔王語(yǔ)言的解釋系統(tǒng),把 他的話解釋成人能聽得懂的話. 2. 基本要求: 用下述兩條具體規(guī)則和上述規(guī)則形式(2)實(shí)現(xiàn).設(shè)大寫字母表示魔王語(yǔ)言的詞匯 小寫字母表示人的語(yǔ)言的詞匯 希臘字母表示可以用大寫字母或小寫字母代換的變量.魔王語(yǔ)言可含人的詞匯. (1) B --> tAdA (2) A --> sae 3. 測(cè)試數(shù)據(jù): B(ehnxgz)B 解釋成 tsaedsaeezegexenehetsaedsae若將小寫字母與漢字建立下表所示的對(duì)應(yīng)關(guān)系,則魔王說(shuō)的話是:"天上一只鵝地上一只鵝鵝追鵝趕鵝下鵝蛋鵝恨鵝天上一只鵝地上一只鵝". | t | d | s | a | e | z | g | x | n | h | | 天 | 地 | 上 | 一只| 鵝 | 追 | 趕 | 下 | 蛋 | 恨 |
上傳時(shí)間： 2014-12-02
上傳用戶：jkhjkh1982
ApMl provides users with the ability to crawl the web and download pages to their computer in a directory structure suitable for a Machine Learning system to both train itself and classify new documents. Classification Algorithms include Naive Bayes, KNN
標(biāo)簽： the provides computer download
上傳時(shí)間： 2015-11-29
上傳用戶：ywqaxiwang
We have a group of N items (represented by integers from 1 to N), and we know that there is some total order defined for these items. You may assume that no two elements will be equal (for all a, b: a<b or b<a). However, it is expensive to compare two items. Your task is to make a number of comparisons, and then output the sorted order. The cost of determining if a < b is given by the bth integer of element a of costs (space delimited), which is the same as the ath integer of element b. Naturally, you will be judged on the total cost of the comparisons you make before outputting the sorted order. If your order is incorrect, you will receive a 0. Otherwise, your score will be opt/cost, where opt is the best cost anyone has achieved and cost is the total cost of the comparisons you make (so your score for a test case will be between 0 and 1). Your score for the problem will simply be the sum of your scores for the individual test cases.
標(biāo)簽： represented integers group items
上傳時(shí)間： 2016-01-17
上傳用戶：jeffery
The XML Toolbox converts MATLAB data types (such as double, char, struct, complex, sparse, logical) of any level of nesting to XML format and vice versa. For example, >> project.name = MyProject >> project.id = 1234 >> project.param.a = 3.1415 >> project.param.b = 42 becomes with str=xml_format(project, off ) "<project> <name>MyProject</name> <id>1234</id> <param> <a>3.1415</a> <b>42</b> </param> </project>" On the other hand, if an XML string XStr is given, this can be converted easily to a MATLAB data type or structure V with the command V=xml_parse(XStr).
標(biāo)簽： converts Toolbox complex logical
上傳時(shí)間： 2016-02-12
上傳用戶：a673761058
一個(gè)用神經(jīng)網(wǎng)絡(luò)方法實(shí)現(xiàn)人臉識(shí)別的程序，來(lái)源于CMU的machine learning 課程作業(yè)，具有參考價(jià)值
標(biāo)簽： 神經(jīng)網(wǎng)絡(luò) 人臉識(shí)別 程序
上傳時(shí)間： 2013-11-28
上傳用戶：515414293
Many of the pattern fi nding algorithms such as decision tree, classifi cation rules and clustering techniques that are frequently used in data mining have been developed in machine learning research community. Frequent pattern and association rule mining is one of the few excep- tions to this tradition. The introduction of this technique boosted data mining research and its impact is tremendous. The algorithm is quite simple and easy to implement. Experimenting with Apriori-like algorithm is the fi rst thing that data miners try to do.
標(biāo)簽： 64257 algorithms decision pattern
上傳時(shí)間： 2014-01-12
上傳用戶：wangdean1101
Semantic analysis of multimedia content is an on going research area that has gained a lot of attention over the last few years. Additionally, machine learning techniques are widely used for multimedia analysis with great success. This work presents a combined approach to semantic adaptation of neural network classifiers in multimedia framework. It is based on a fuzzy reasoning engine which is able to evaluate the outputs and the confidence levels of the neural network classifier, using a knowledge base. Improved image segmentation results are obtained, which are used for adaptation of the network classifier, further increasing its ability to provide accurate classification of the specific content.
標(biāo)簽： multimedia Semantic analysis research
上傳時(shí)間： 2016-11-24
上傳用戶：蟲蟲蟲蟲蟲蟲
漢諾塔！！！ Simulate the movement of the Towers of Hanoi puzzle Bonus is possible for using animation eg. if n = 2 A→B A→C B→C if n = 3 A→C A→B C→B A→C B→A B→C A→C
標(biāo)簽： the animation Simulate movement
上傳時(shí)間： 2017-02-11
上傳用戶：waizhang
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