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  • 机器学习的开源工具

    <Category: NLP, 机器学习, 资源分享> 查看评论

    以下工具绝大多数都是开源的,基于GPL、Apache等开源协议,使用时请仔细阅读各工具的license statement

    I. Information Retrieval

    1. Lemur/Indri

    The Lemur Toolkit for Language Modeling and Information Retrieval
    Lemur’s latest search engine

    2. Lucene/Nutch
    Apache Lucene is a high-performance, full-featured text search engine library written entirely in Java.
    Lucene是apache的顶级开源项目,基于Apache 2.0协议,完全用java编写,具有perl, c/c++, dotNet等多个port

    3. WGet
    GNU Wget is a free software package for retrieving files using HTTP, HTTPS and FTP, the most widely-used Internet protocols. It is a non-interactive commandline tool, so it may easily be called from scripts, cron jobs, terminals without X-Windows support, etc.

    II. Natural Language Processing
    1. EGYPT: A Statistical Machine Translation Toolkit

    2. GIZA++ (Statistical Machine Translation)
    GIZA++ is an extension of the program GIZA (part of the SMT toolkit EGYPT) which was developed by the Statistical Machine Translation team during the summer workshop in 1999 at the Center for Language and Speech Processing at Johns-Hopkins University (CLSP/JHU). GIZA++ includes a lot of additional features. The extensions of GIZA++ were designed and written by Franz Josef Och.
    Franz Josef Och先后在德国Aachen大学,ISI(南加州大学信息科学研究所)和Google工作。GIZA++现已有Windows移植版本,对IBM 的model 1-5有很好支持。

    3. PHARAOH (Statistical Machine Translation)
    a beam search decoder for phrase-based statistical machine translation models

    4. OpenNLP:

    btw: 这些SMT的工具还都喜欢用埃及相关的名字命名,像什么GIZA、PHARAOH、Cairo等等。Och在ISI时开发了GIZA++,PHARAOH也是由来自ISI的Philipp Koehn 开发的,关系还真是复杂啊

    5. MINIPAR by Dekang Lin (Univ. of Alberta, Canada)
    MINIPAR is a broad-coverage parser for the English language. An evaluation with the SUSANNE corpus shows that MINIPAR achieves about 88% precision and 80% recall with respect to dependency relationships. MINIPAR is very efficient, on a Pentium II 300 with 128MB memory, it parses about 300 words per second.

    6. WordNet
    WordNet is an online lexical reference system whose design is inspired by current psycholinguistic theories of human lexical memory. English nouns, verbs, adjectives and adverbs are organized into synonym sets, each representing one underlying lexical concept. Different relations link the synonym sets.
    WordNet was developed by the Cognitive Science Laboratory at Princeton University under the direction of Professor George A. Miller (Principal Investigator).
    WordNet最新版本是2.1 (for Windows & Unix-like OS),提供bin, src和doc。

    7. HowNet
    HowNet is an on-line common-sense knowledge base unveiling inter-conceptual relations and inter-attribute relations of concepts as connoting in lexicons of the Chinese and their English equivalents.
    由CAS的Zhendong Dong & Qiang Dong开发,是一个类似于WordNet的东东

    8. Statistical Language Modeling Toolkit
    The CMU-Cambridge Statistical Language Modeling toolkit is a suite of UNIX software to facilitate the construction and testing of statistical language models.

    9. SRI Language Modeling Toolkit
    SRILM is a toolkit for building and applying statistical language models (LMs), primarily for use in speech recognition, statistical tagging and segmentation. It has been under development in the SRI Speech Technology and Research Laboratory since 1995.

    10. ReWrite Decoder
    The ISI ReWrite Decoder Release 1.0.0a by Daniel Marcu and Ulrich Germann. It is a program that translates from one natural languge into another using statistical machine translation.

    11. GATE (General Architecture for Text Engineering)
    A Java Library for Text Engineering

    III. Machine Learning
    1. YASMET: Yet Another Small MaxEnt Toolkit (Statistical Machine Learning)
    由Franz Josef Och编写。此外,OpenNLP项目里有一个java的MaxEnt工具,使用GIS估计参数,由东北大学的张乐(目前在英国留学)port为C++版本

    2. LibSVM
    由国立台湾大学(ntu)的Chih-Jen Lin开发,有C++,Java,perl,C#等多个语言版本
    LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC ), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM ). It supports multi-class classification.

    3. SVM Light
    由cornell的Thorsten Joachims在dortmund大学时开发,成为LibSVM之后最为有名的SVM软件包。开源,用C语言编写,用于ranking问题

    4. CLUTO
    a software package for clustering low- and high-dimensional datasets

    5. CRF++
    Yet Another CRF toolkit for segmenting/labelling sequential data
    CRF(Conditional Random Fields),由HMM/MEMM发展起来,广泛用于IE、IR、NLP领域

    6. SVM Struct
    同SVM Light,均由cornell的Thorsten Joachims开发。
    SVMstruct is a Support Vector Machine (SVM) algorithm for predicting multivariate outputs. It performs supervised learning by approximating a mapping
    h: X –> Y
    using labeled training examples (x1,y1), …, (xn,yn).
    Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression, SVMstruct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging.
    SVMstruct can be thought of as an API for implementing different kinds of complex prediction algorithms. Currently, we have implemented the following learning tasks:
    SVMmulticlass: Multi-class classification. Learns to predict one of k mutually exclusive classes. This is probably the simplest possible instance of SVMstruct and serves as a tutorial example of how to use the programming interface.
    SVMcfg: Learns a weighted context free grammar from examples. Training examples (e.g. for natural language parsing) specify the sentence along with the correct parse tree. The goal is to predict the parse tree of new sentences.
    SVMalign: Learning to align sequences. Given examples of how sequence pairs align, the goal is to learn the substitution matrix as well as the insertion and deletion costs of operations so that one can predict alignments of new sequences.
    SVMhmm: Learns a Markov model from examples. Training examples (e.g. for part-of-speech tagging) specify the sequence of words along with the correct assignment of tags (i.e. states). The goal is to predict the tag sequences for new sentences.

    IV. Misc:
    1. Notepad++: 一个开源编辑器,支持C#,perl,CSS等几十种语言的关键字,功能可与新版的UltraEdit,Visual Studio .NET媲美

    2. WinMerge: 用于文本内容比较,找出不同版本的两个程序的差异

    3. OpenPerlIDE: 开源的perl编辑器,内置编译、逐行调试功能
    ps: 论起编辑器偶见过的最好的还是VS .NET了,在每个function前面有+/-号支持expand/collapse,支持区域copy/cut/paste,使用ctrl+ c/ctrl+x/ctrl+v可以一次选取一行,使用ctrl+k+c/ctrl+k+u可以comment/uncomment多行,还有还有…… Visual Studio .NET is really kool:D

    4. Berkeley DB
    Berkeley DB不是一个关系数据库,它被称做是一个嵌入式数据库:对于c/s模型来说,它的client和server共用一个地址空间。由于数据库最初是从文件系统中发展起来的,它更像是一个key-value pair的字典型数据库。而且数据库文件能够序列化到硬盘中,所以不受内存大小限制。BDB有个子版本Berkeley DB XML,它是一个xml数据库:以xml文件形式存储数据?BDB已被包括microsoft、google、HP、ford、motorola等公司嵌入到自己的产品中去了
    Berkeley DB (libdb) is a programmatic toolkit that provides embedded database support for both traditional and client/server applications. It includes b+tree, queue, extended linear hashing, fixed, and variable-length record access methods, transactions, locking, logging, shared memory caching, database recovery, and replication for highly available systems. DB supports C, C++, Java, PHP, and Perl APIs.
    It turns out that at a basic level Berkeley DB is just a very high performance, reliable way of persisting dictionary style data structures – anything where a piece of data can be stored and looked up using a unique key. The key and the value can each be up to 4 gigabytes in length and can consist of anything that can be crammed in to a string of bytes, so what you do with it is completely up to you. The only operations available are “store this value under this key”, “check if this key exists” and “retrieve the value for this key” so conceptually it’s pretty simple – the complicated stuff all happens under the hood.
    case study:
    Ask Jeeves uses Berkeley DB to provide an easy-to-use tool for searching the Internet.
    Microsoft uses Berkeley DB for the Groove collaboration software
    AOL uses Berkeley DB for search tool meta-data and other services.
    Hitachi uses Berkeley DB in its directory services server product.
    Ford uses Berkeley DB to authenticate partners who access Ford’s Web applications.
    Hewlett Packard uses Berkeley DB in serveral products, including storage, security and wireless software.
    Google uses Berkeley DB High Availability for Google Accounts.
    Motorola uses Berkeley DB to track mobile units in its wireless radio network products.

    11. R
    R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.
    R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.
    One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.
    R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.


    本文来自: 机器学习的开源工具

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