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Weka 3 - Data Mining with Open Source Machine Learning Software in Java

Weka 3 - Data Mining with Open Source Machine Learning Software in Java
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes. Found only on the islands of New Zealand, the Weka is a flightless bird with an inquisitive nature. Weka is open source software issued under the GNU General Public License. Yes, it is possible to apply Weka to big data! Data Mining with Weka is a 5 week MOOC, which was held first in late 2013. Related:  Data Mining Tools

weka - home Getting Started with RapidMiner Studio - RapidMiner Documentation So here you are, a business analyst or developer or jack-of-all-trades. Things are going well, but it's time to fine-tune your business. You've probably collected a myriad of data points about your customer base and would like to determine which customers will remain loyal and which ones will likely churn. Attracting new customers comes at a price, so you'd really like to maintain your current customer base. How do you predict who may leave? What target audiences do you spend marketing dollars on to entice them to stay? Using RapidMiner's modern enterprise platform, you can quickly and easily create analytic workflows called processes to determine who to target. This series of five Getting Started tutorials will help familiarize you with some basic features and functionality of RapidMiner Studio. Below are some additional resources available to help you get up and running quickly with RapidMiner Studio.

Rada Mihalcea: Downloads downloads [see also the research page for related information] Various software modules and data sets that are/were used in my research. For any questions regarding the content of this page, please contact Rada Mihalcea, rada at cs.unt.edu new Efficient Indexer for the Google Web 1T Ngram corpus new Wikipedia Interlingual Links Evaluation Dataset new Sentiment Lexicons in Spanish Measuring the Semantic Relatedness between Words and Images Text Mining for Automatic Image Tagging Learning to Identify Educational Materials (LIEM) Cross-Lingual Semantic Relatedness (CLSR) Data for Automatic Short Answer Grading Multilingual Subjectivity Analysis: Gold Standard and Training Data GWSD: Graph-based Unsupervised Word Sense Disambiguation Affective Text: data annotated for emotions and polarity SenseLearner: all words word sense disambiguation tool Benchmark for the evaluation of back-of-the-book indexing systems FrameNet - WordNet verb sense mapping Resources and Tools for Romanian NLP TWA sense tagged data set

Vowpal Wabbit (Fast Learning) Vowpal Wabbit (Fast Learning) This is a project started at Yahoo! Research and continuing at Microsoft Research to design a fast, scalable, useful learning algorithm. VW is the essence of speed in machine learning, able to learn from terafeature datasets with ease. Via parallel learning, it can exceed the throughput of any single machine network interface when doing linear learning, a first amongst learning algorithms. We primarily use the wiki off github. DownloadCommand lineTutorialExamplesInput ValidatorDiscussionsMailing list BabelNet BabelNet is a multilingual semantic network obtained as an integration of WordNet and Wikipedia. Statistics of BabelNet[edit] As of October 2013[update], BabelNet (version 2.0) covers 50 languages, including all European languages, most Asian languages, and even Latin. BabelNet 2.0 contains more than 9 million synsets and about 50 million word senses (regardless of their language). Each Babel synset contains 5.5 synonyms, i.e., word senses, on average, in any language. Applications[edit] BabelNet has been shown to enable multilingual Natural Language Processing applications. See also[edit] References[edit] Jump up ^ R. External links[edit]

Tutorial · JohnLangford/vowpal_wabbit Wiki We did a new version 7.8 tutorial which includes: New tutorials associated with Version 7.4. This includes: A new version 7.0 tutorial is available. It covers the basics and most common options, how to use VW and the data format for different types of problems, such as Binary Classification, Regression, Multiclass Classification, Cost-Sensitive Multiclass Classification, "Offline" Contextual Bandit and Sequence Predictions. Many more advanced options in terms of flags and the data format are not covered. The version 6.1 tutorial and various pieces below covers some topics not covered in the version 7 tutorial, as most of these haven't change in the latest version: Older stuff The version 5.1 tutorial with a video. Version 5.0 Videolecture. The main piece (v5.0)The importance weight invariant update rule. A Step by step introduction The first step is downloading a version of VW. git clone Now we compile: cd vowpal_wabbit . Note: make vw make test

Academic Video Search Robert H. Goddard was born in Worcester, Massachusetts to Nahum Danford Goddard, a businessman, and Fannie Hoyt Goddard. Early in life, young Robert suffered from pulmonary tuberculosis which kept him out of school for long periods of time. After graduating from school, Robert Goddard applied and was accepted at Worcester Polytechnic Institute. Unfortunately, in early 1913, Goddard became seriously ill with tuberculosis, and had to leave his position at Princeton. Goddard's thoughts on space flight started to emerge in 1915, when he theorized that a rocket would work in a vacuum, and didn't need to push against air in order to fly. Goddard turned his attention to the components of his rockets. Powder rockets were still problematic. Indeed, the flight of Goddard’s rocket on March 16, 1926, at Auburn, Mass., was as significant to history as that of the Wright brothers at Kitty Hawk. References and Further Reading: For more interesting articles join the Yovisto Blog:

OpenNN | Artelnics OpenNN is an open source class library written in C++ programming language which implements neural networks, a main area of deep learning research. It is intended for advanced users, with high C++ and machine learning skills. The library implements any number of layers of non-linear processing units for supervised learning. This deep architecture allows the design of neural networks with universal approximation properties. OpenNN contains data mining algorithms as a bundle of classes. OpenNN screenshot The package comes with unit testing, many examples and extensive documentation. Commercial support Artelnics offers commercial support for the development of applications using OpenNN. Create your own predicitive analytics solution using the most powerful deep learning technology: See whether OpenNN can fit your project requirements. Develop a prototype to get your idea working and eliminate technical risks. Build your solution by taking advantage of the right knowledge and resources.

dbpedia « Griff's Graphs First of all, thanks very much for the feedback you all gave me on my graph of ideas. I wasn’t quite aware of how many people are interested in this sort of stuff. I now have lots of great ideas for new projects which will keep me busy for a long while. I must say making the graphs is the easy part – it is obtaining the data which takes time. I’ve made a note of all of your suggestions and will try to create something out of them soon. If you haven’t already, you can submit an idea here. Housekeeping There were a great number of comments about my last graph and so I’ll try to answer the main questions here. “It is way too biased towards Western ideas.” – Yes, see point one of the original blog post. “Where are all of the musicians and artists?” “The title is very misleading.”– The original post had an asterisk on the word ‘every’ which was meant to highlight the fact the graph had caveats. Now that is out-of-the-way, I’d like to present my latest work. Network: Graph Of Ideas vs. Caveats

Top 10 data mining algorithms in plain English Today, I’m going to explain in plain English the top 10 most influential data mining algorithms as voted on by 3 separate panels in this survey paper. Once you know what they are, how they work, what they do and where you can find them, my hope is you’ll have this blog post as a springboard to learn even more about data mining. What are we waiting for? Let’s get started! Update 16-May-2015: Thanks to Yuval Merhav and Oliver Keyes for their suggestions which I’ve incorporated into the post. Update 28-May-2015: Thanks to Dan Steinberg (yes, the CART expert!) What does it do? Wait, what’s a classifier? What’s an example of this? Now: Given these attributes, we want to predict whether the patient will get cancer. And here’s the deal: Using a set of patient attributes and the patient’s corresponding class, C4.5 constructs a decision tree that can predict the class for new patients based on their attributes. Cool, so what’s a decision tree? The bottomline is: Is this supervised or unsupervised? 3.

Silk - A Link Discovery Framework for the Web of Data The Silk framework is a tool for discovering relationships between data items within different Linked Data sources. Data publishers can use Silk to set RDF links from their data sources to other data sources on the Web. News 2014-02-21: Version 2.6 released including a new version of the Silk Workbench that offers a REST API and a Plugin System. Contents About Silk The Web of Data is built upon two simple ideas: First, to employ the RDF data model to publish structured data on the Web. The Silk Link Discovery Framework supports data publishers in accomplishing the second task. Silk can be used through the Silk Workbench graphical user interface or from the command line. Silk Workbench Silk Workbench is a web application which guides the user through the process of interlinking different data sources. Silk Workbench offers the following features: It enables the user to manage different sets of data sources and linking tasks. Silk Command Line Applications Silk Free Text Preprocessor Support

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