Prover9 Download Prover9, Mace4, and several related programs come packaged in a system called LADR (Library for Automated Deduction Research). If you install one of these LADR packages, you will get command-line programs. (The programs are run by typing commands to a command prompt, terminal, or shell.) A GUI (graphical user interface) called Prover9-Mace4 is also available. (The GUI is self-contained, so there is no need to install one of these LADR packages to use the GUI.) Manuals and Examples Prover9 for Unix-like Systems (Linux, Mac OS X) For differences between the versions, see the Changelog file. Download the .tar.gz file, unpack it, go to the LADR directory, and "make all". Prover9 for MS Windows (This might not be current with the Unix version.) Prover9, Mace4, and related programs have been compiled under Cygwin in MS Windows. Notes Binaries of Prover9, Mace4, and several other programs are included. If you have suggestions for improving the Windows version, let us know.
LIBSVM Tools This page provides some miscellaneous tools based on LIBSVM (and LIBLINEAR). Roughly they include Disclaimer: We do not take any responsibility on damage or other problems caused by using these software and data sets. Please download the zip file. Please download the zip file. Please download the zip file. T. You can use either MATLAB or Python. LIBSVM Data: Classification (Binary Class) This page contains many classification, regression, multi-label and string data sets stored in LIBSVM format. Many are from UCI, Statlog, StatLib and other collections. We thank their efforts. For most sets, we linearly scale each attribute to [-1,1] or [0,1]. a1a Source: UCI / AdultPreprocessing: The original Adult data set has 14 features, among which six are continuous and eight are categorical. a2a Source: UCI / AdultPreprocessing: The same as a1a. a3a Source: UCI / AdultPreprocessing: The same as a1a. a4a Source: UCI / AdultPreprocessing: The same as a1a. a5a Source: UCI / AdultPreprocessing: The same as a1a. a6a Source: UCI / AdultPreprocessing: The same as a1a. a7a Source: UCI / AdultPreprocessing: The same as a1a. a8a Source: UCI / AdultPreprocessing: The same as a1a. a9a Source: UCI / AdultPreprocessing: The same as a1a. australian Source: Statlog / Australian# of classes: 2# of data: 690# of features: 14Files: breast-cancer cod-rna colon-cancer covtype.binary diabetes duke breast-cancer heart
Sphinx-4 - A speech recognizer written entirely in the Java(TM) programming language Overview Sphinx4 is a pure Java speech recognition library. It provides a quick and easy API to convert the speech recordings into text with the help CMUSphinx acoustic models. It can be used on servers and in desktop applications. Sphinx4 supports US English and many other languages. Using in your projects As any library in Java all you need to do to use sphinx4 is to add jars into dependencies of your project and then you can write code using the API. The easiest way to use modern sphinx4 is to use modern build tools like Apache Maven or Gradle. <project> ... Then add sphinx4-core to the project dependencies: <dependency><groupId>edu.cmu.sphinx</groupId><artifactId>sphinx4-core</artifactId><version>5prealpha-SNAPSHOT</version></dependency> Add sphinx4-data to dependencies as well if you want to use default acoustic and language models: <dependency><groupId>edu.cmu.sphinx</groupId><artifactId>sphinx4-data</artifactId><version>5prealpha-SNAPSHOT</version></dependency> Basic Usage or Demos
Conditional random field Conditional random fields (CRFs) are a class of statistical modelling method often applied in pattern recognition and machine learning, where they are used for structured prediction. Whereas an ordinary classifier predicts a label for a single sample without regard to "neighboring" samples, a CRF can take context into account; e.g., the linear chain CRF popular in natural language processing predicts sequences of labels for sequences of input samples. CRFs are a type of discriminative undirected probabilistic graphical model. It is used to encode known relationships between observations and construct consistent interpretations. It is often used for labeling or parsing of sequential data, such as natural language text or biological sequences[1] and in computer vision.[2] Specifically, CRFs find applications in shallow parsing,[3] named entity recognition[4] and gene finding, among other tasks, being an alternative to the related hidden Markov models. Description[edit] and random variables
Iris Data Set Source: Creator: R.A. Fisher Donor: Michael Marshall (MARSHALL%PLU '@' io.arc.nasa.gov) Data Set Information: This is perhaps the best known database to be found in the pattern recognition literature. Predicted attribute: class of iris plant. This is an exceedingly simple domain. This data differs from the data presented in Fishers article (identified by Steve Chadwick, spchadwick '@' espeedaz.net ). Attribute Information: 1. sepal length in cm 2. sepal width in cm 3. petal length in cm 4. petal width in cm 5. class: -- Iris Setosa -- Iris Versicolour -- Iris Virginica Relevant Papers: Fisher,R.A. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System Structure and Classification Rule for Recognition in Partially Exposed Environments". Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". See also: 1988 MLC Proceedings, 54-64. Papers That Cite This Data Set1: Ping Zhong and Masao Fukushima. Sotiris B. .
Nike Running Benoît Sagot - WOLF Le WOLF (Wordnet Libre du Français) est une ressource lexicale sémantique (wordnet) libre pour le français. Le WOLF a été construit à partir du Princeton WordNet (PWN) et de diverses ressources multilingues (Sagot et Fišer 2008a, Sagot et Fišer 2008b, Fišer et Sagot 2008). Les lexèmes polysémiques ont été traités au moyen d'une approche reposant sur l'alignement en mots d'un corpus parallèle en cinq langues. En 2009, un travail spécifique a été effectué sur les synsets adverbiaux (Sagot, Fort et Venant 2009a, Sagot, Fort et Venant 2009b) De nombreux enrichissements ont été apportés depuis, notamment en 2012, y compris une validation manuelle partielle, donnant ainsi naissance aux versions 1.0b et ultérieures (plus d'infos ici sous peu). Le WOLF contient tous les synsets du Princetown WordNet, y compris ceux pour lesquels aucun lexème français n'est connu. Le WOLF est une ressource libre, distribuée sous licence Cecill-C (compatible LGPL).
guide.pdf About WordNet - WordNet - About WordNet [译] libsvm-3.12中的 README（一）_他们都说我栓不住_新浪博客 Libsvm 是一个简单的，易用的，高效的SVM分类和回归软件。它解决了 C-SVM 分类，nu-SVM 分类， one-class-SVM，epsilon-SVM 回归，nu-SVM 回归（的问题）。它也提供了一个自动的 C-SVM 分类的模型选择工具。本文档解释了 Libsvm 的用法。 Libsvm 的获取： 请在使用 Libsvm 之前阅读 COPYRIGHT 文档。 - 快速开始 - 安装与数据格式 - 使用 ‘svm-train’ - 使用 ‘svm-predict’ - 使用 ‘svm-scale’ - 实际应用的小贴士 - 例子 - 自定义核函数 - 库的使用 - Java 版本 - 编译 Windows 二进制文件 - 附加工具：Sub-sampling, Parameter Selection, Format checking, 等 - MATLAB/OCTAVE 接口 - Python 接口 - 补充 快速开始======================= 如果你刚接触 SVM 并且数据不大，安装完毕之后请用 ‘tools' 文件夹下的 easy.py。 使用方法：easy.py training_file [testing_file] 有关参数选择的更多信息请参阅 ‘tools/README’。 安装和数据格式======================= 在 Unix 系统中，键入 ‘make’ 编译 ‘svm-train’ 和 ‘smv-predict’ 程序。 在另一些系统中，查阅 ‘Makefile’ 来编译它们（ 例如，参阅本文档中的 ‘编译 Windows 二进制文件’ ）或者使用已编译的二进制文件（ Windows 二进制文件在 ‘windows’ 目录下 ）。 训练数据和测试数据的文件格式如下： <label><index1>:<value1><index2>:<value2> ... 每一行包括一个实例（样本）并以 ‘\n’ 字符结束。 本程序包中的一个已分类的数据的样本是 ‘heart_scale’。 键入 ‘svm-train heart_scale’，此程序会读入训练数据并且输出（建立好的）模型文件 ‘heart_scale.model’。 -v n：n-折交叉验证模式
QMSS e-Lessons | About the Chi-Square Test Generally speaking, the chi-square test is a statistical test used to examine differences with categorical variables. There are a number of features of the social world we characterize through categorical variables - religion, political preference, etc. To examine hypotheses using such variables, use the chi-square test. The chi-square test is used in two similar but distinct circumstances: for estimating how closely an observed distribution matches an expected distribution - we'll refer to this as the goodness-of-fit testfor estimating whether two random variables are independent. The Goodness-of-Fit Test One of the more interesting goodness-of-fit applications of the chi-square test is to examine issues of fairness and cheating in games of chance, such as cards, dice, and roulette. So how can the goodness-of-fit test be used to examine cheating in gambling? One night at the Tunisian Nights Casino, renowned gambler Jeremy Turner (a.k.a. Recap Testing Independence Example 1. 2. 3. 4.
windows下LIBSVM使用方法及例子 - ddkxddkx的专栏 1. 程序介绍和环境设置 windows下的libsvm是在命令行运行的Console Program。 libsvm-2.9/windows/文件夹中的：svm-train.exesvm-predict.exesvm-scale.exe libsvm-2.9/windows/文件夹中的：checkdata.pysubset.pyeasy.pygrid.py 另外有：svm-toy.exe，我暂时知道的是用于演示svm分类。 因为程序运行要用到python脚本用来寻找参数，使用gnuplot来绘制图形。 为了方便，将gnuplot的bin、libsvm-2.9/windows/加入到系统的path中，如下： gnuplot.JPG libsvm.JPG 这样，可以方便的从命令行的任何位置调用gnuplot和libsvm的可执行程序，如下调用svm-train.exe: pathtest.JPG 出现svm-train程序中的帮助提示，说明path配置成功。 至此，libsvm运行的环境配置完成。 2. 我们所使用的数据为UCI的iris数据集，将其类别标识换为1、2、3。 训练文件tra_iris.txt1 1:5.4 2:3.4 3:1.7 4:0.21 1:5.1 2:3.7 3:1.5 4:0.41 1:4.6 2:3.6 3:1 4:0.21 1:5.1 2:3.3 3:1.7 4:0.51 1:4.8 2:3.4 3:1.9 4:0.2……2 1:5.9 2:3.2 3:4.8 4:1.82 1:6.1 2:2.8 3:4 4:1.32 1:6.3 2:2.5 3:4.9 4:1.52 1:6.1 2:2.8 3:4.7 4:1.22 1:6.4 2:2.9 3:4.3 4:1.3……3 1:6.9 2:3.2 3:5.7 4:2.33 1:5.6 2:2.8 3:4.9 4:23 1:7.7 2:2.8 3:6.7 4:23 1:6.3 2:2.7 3:4.9 4:1.83 1:6.7 2:3.3 3:5.7 4:2.13 1:7.2 2:3.2 3:6 4:1.8…… libsvm的参数选择一直是令人头痛的问题。 使用方法为：easy.py training_file [testing_file] 另外有其他参数选择工具，可以参考tools中的readme说明。 easy.JPG
Latent semantic analysis Latent semantic analysis (LSA) is a technique in natural language processing, in particular in vectorial semantics, of analyzing relationships between a set of documents and the terms they contain by producing a set of concepts related to the documents and terms. LSA assumes that words that are close in meaning will occur in similar pieces of text. A matrix containing word counts per paragraph (rows represent unique words and columns represent each paragraph) is constructed from a large piece of text and a mathematical technique called singular value decomposition (SVD) is used to reduce the number of columns while preserving the similarity structure among rows. Words are then compared by taking the cosine of the angle between the two vectors formed by any two rows. Values close to 1 represent very similar words while values close to 0 represent very dissimilar words.[1] Overview[edit] Occurrence matrix[edit] Rank lowering[edit] Derivation[edit] Let be a matrix where element in document ). and