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.
標簽： Classifiers Implemented Bayesian applying
上傳時間： 2015-09-11
上傳用戶：ommshaggar
上下文無關文法（Context-Free Grammar, CFG）是一個4元組G=(V, T, S, P)，其中，V和T是不相交的有限集，S∈V，P是一組有限的產生式規則集，形如A→α，其中A∈V，且α∈(V∪T)*。V的元素稱為非終結符，T的元素稱為終結符，S是一個特殊的非終結符，稱為文法開始符。 設G=(V, T, S, P)是一個CFG，則G產生的語言是所有可由G產生的字符串組成的集合，即L(G)={x∈T* | Sx}。一個語言L是上下文無關語言（Context-Free Language, CFL），當且僅當存在一個CFG G，使得L=L(G)。 *⇒ 例如，設文法G：S→AB A→aA|a B→bB|b 則L(G)={a^nb^m | n,m>=1} 其中非終結符都是大寫字母，開始符都是S，終結符都是小寫字母。
標簽： Context-Free Grammar CFG
上傳時間： 2013-12-10
上傳用戶：gaojiao1999
一:需求分析 1. 問題描述 魔王總是使用自己的一種非常精練而抽象的語言講話,沒人能聽懂,但他的語言是可逐步解釋成人能聽懂的語言,因為他的語言是由以下兩種形式的規則由人的語言逐步抽象上去的: ----------------------------------------------------------- (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.試寫一個魔王語言的解釋系統,把 他的話解釋成人能聽得懂的話. 2. 基本要求: 用下述兩條具體規則和上述規則形式(2)實現.設大寫字母表示魔王語言的詞匯 小寫字母表示人的語言的詞匯 希臘字母表示可以用大寫字母或小寫字母代換的變量.魔王語言可含人的詞匯. (1) B --> tAdA (2) A --> sae 3. 測試數據: B(ehnxgz)B 解釋成 tsaedsaeezegexenehetsaedsae若將小寫字母與漢字建立下表所示的對應關系,則魔王說的話是:"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
標簽： the provides computer download
上傳時間： 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.
標簽： represented integers group items
上傳時間： 2016-01-17
上傳用戶：jeffery
一個用神經網絡方法實現人臉識別的程序，來源于CMU的machine learning 課程作業，具有參考價值
上傳時間： 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.
標簽： 64257 algorithms decision pattern
上傳時間： 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.
標簽： multimedia Semantic analysis research
上傳時間： 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
標簽： the animation Simulate movement
上傳時間： 2017-02-11
上傳用戶：waizhang
pdf格式的英文文獻，是關于認知無線電網絡的，編者是加拿大桂爾夫大學的Qusay H. Mahmoud。ISBN：978-0-470-06196-1 章節內容： 1 Biologically Inspired Networking 2 The Role of Autonomic Networking in Cognitive Networks 3 Adaptive Networks 4 Self-Managing Networks 5 Machine Learning for Cognitive Networks: Technology Assessment and Research Challenges 6 Cross-Layer Design and Optimization in Wireless Networks 等，共計13章，全書348頁，pdf文件383頁。
標簽： 英文
上傳時間： 2014-01-27
上傳用戶：daguda
