weka - home GGobi data visualization system. Implementation of k-means Clustering - Edureka In this blog, you will understand what is K-means clustering and how it can be implemented on the criminal data collected in various US states. The data contains crimes committed like: assault, murder, and rape in arrests per 100,000 residents in each of the 50 US states in 1973. Along with analyzing the data you will also learn about: Finding the optimal number of clusters.Minimizing distortionCreating and analyzing the elbow curve.Understanding the mechanism of k-means algorithm. Let us start with the analysis. The data looks as: Click on the image to download this dataset Need this dataset? First let’s prepare the data for the analysis. > crime0 <- na.omit(USArrests) > crime <- data.matrix (crime0) > str(crime) num [1:50, 1:4] 13.2 10 8.1 8.8 9 7.9 3.3 5.9 15.4 17.4 ... - attr(*, "dimnames")=List of 2 ..$ : chr [1:50] "Alabama" "Alaska" "Arizona" "Arkansas" ... ..$ : chr [1:4] "Murder" "Assault" "UrbanPop" "Rape" Let us take the number of clusters to be 5. Analyzing the Clustering :
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
Home - SCaVis Freedom to choose a programming language. Freedom to choose an operating system. Freedom to share your code. Supported programming languages SCaVis can be used with several scripting languages for the Java platform, such as BeanShell, Jython (the Python programming language), Groovy and JRuby (Ruby programming language). Supported platforms SCaVis runs on Windows, Linux, Mac and Android operating systems. SCaVis is a successor of the popular jHepWork package which has been under intensive development since 2005.
A Guide to Deep Learning by YerevaNN When you are comfortable with the prerequisites, we suggest four options for studying deep learning. Choose any of them or any combination of them. The number of stars indicates the difficulty. Hugo Larochelle's video course on YouTube. There are many software frameworks that provide necessary functions, classes and modules for machine learning and for deep learning in particular. Jupyter notebooks are a convenient way to play with Python code.
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]
The R Project for Statistical Computing What is machine learning? Everything you need to know This device is unable to play the requested video. Machine learning is enabling computers to tackle tasks that have, until now, only been carried out by people. From driving cars to translating speech, machine learning is driving an explosion in the capabilities of artificial intelligence -- helping software make sense of the messy and unpredictable real world. But what exactly is machine learning and what is making the current boom in machine learning possible? What is machine learning? At a very high level, machine learning is the process of teaching a computer system how to make accurate predictions when fed data. Those predictions could be answering whether a piece of fruit in a photo is a banana or an apple, spotting people crossing the road in front of a self-driving car, whether the use of the word book in a sentence relates to a paperback or a hotel reservation, whether an email is spam, or recognizing speech accurately enough to generate captions for a YouTube video. What is AlphaGo?
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:
Une introduction aux arbres de décision Les arbres de décision sont l’une des structures de données majeures de l’apprentissage statistique. Leur fonctionnement repose sur des heuristiques qui, tout en satisfaisant l’intuition, donnent des résultats remarquables en pratique (notamment lorsqu’ils sont utilisés en « forêts aléatoires »). Leur structure arborescente les rend également lisibles par un être humain, contrairement à d’autres approches où le prédicteur construit est une « boîte noire ». L’introduction que nous proposons ici décrit les bases de leur fonctionnement tout en apportant quelques justifications théoriques. Nous aborderons aussi (brièvement) l’extension aux Random Forests. Suivez le lien pour la version PDF. Table des matières Un arbre de décision modélise une hiérarchie de tests sur les valeurs d’un ensemble de variables appelées attributs. 1 Construction d’un arbre de décision Condition d’arrêt : elle influe sur la profondeur et la précision du prédicteur produit. Meilleur attribut : 2 Régression Taux d’erreur :