A Tour of Machine Learning Algorithms. In this post, we take a tour of the most popular machine learning algorithms. It is useful to tour the main algorithms in the field to get a feeling of what methods are available. There are so many algorithms available that it can feel overwhelming when algorithm names are thrown around and you are expected to just know what they are and where they fit. I want to give you two ways to think about and categorize the algorithms you may come across in the field. The first is a grouping of algorithms by the learning style.The second is a grouping of algorithms by similarity in form or function (like grouping similar animals together).
Both approaches are useful, but we will focus in on the grouping of algorithms by similarity and go on a tour of a variety of different algorithm types. After reading this post, you will have a much better understanding of the most popular machine learning algorithms for supervised learning and how they are related. Algorithms Grouped by Learning Style 1. 2. 3. Yun-Nung Chen. Project 1: AutoCAD Drawing (2007) After taking some pictures, we used AutoCAD to draw the five views: front, left-side, right-side, rear, and plan views in detail. - Teamwork with Che-An Lu. * Rerults: [front], [left], [right], [rear], [plan], [report] Project 2: SketchUp Drawing (2007) According the pictures from project 1, we use SketchUp from Google to draw the 3D model. * Results: [image1], [image2], [image3], [report] Project 3: Blender Animation (2007) Using Blender to produce 3D models and make two short animation videos. - Teamwork with Che-An Lu. * Results: [image1], [video1], [video2], [report] Final Project: Animation Producing (2008) Using Blender to produce 3D models, plot a story, and make a refined animation videos. - Teamwork with Che-An Lu and Fang-Err Lin. * Results: [image1], [image2], [image3], [image4], [video] [report] * Award: selected as Best Final Project Award.
CognitiveJ – Image Analysis for Java | Ian's Blog. CognitiveJ is an open source Java library that makes it easy to detect, interpret and identify faces or features contained within raw images. Powered by Project Oxford, The library can suggest a persons age, gender and emotional state. Based on machine learning, the library can also attempt to interpret and describe what is contained within an image.
Its being released for public preview under the Apache 2 licence and at the time of writing, the features include; Faces Facial Detection with Age and Gender Vision Image Describe – Describe visual content of an image and return real world caption to what the image contains Image Analysis – Extract key details from an image and if the image is of an adult/racy natureOCR – Detect and extract a text stream from an imageThumbnail – Create thumbnail images based on key points of interest from an image Overlay Other Features Supports local and remote imagesValidation of parametersImage Grids Getting Started Pre-requisites Structure Wrappers Faces API.
Apache Flink: Scalable Batch and Stream Data Processing. Kaggle: Go from Big Data to Big Analytics. Drewconway (Drew Conway) Text analysis, wordcount, keyword density analyzer, prominence analysis. Apache Flink: Home. Resolving the Scope and Focus of Negation. 24/05/2012: The data are available for download. 09/05/2012: The results and the list of accepted papers have been added. 03/04/2012: Updated information about the system description paper. New deadline: 16 April 2012. 03/04/2012: The final version of the evaluation script and the results have been sent to participants. 16/03/2012: The test dataset has been distributed to participants. 15/03/2012: A new version of the evaluation script for the scope detection task has been distributed to participants. 13/03/2012: A new version of the evaluation script for the scope detection task has been distributed to participants. 09/03/2012: A new version of the CD-SCO dataset has been distributed to participants. 29/02/2012: A new version of the CD-SCO dataset has been distributed to participants. 22/02/2012: A new version of the CD-SCO dataset has been distributed to participants. 19/02/2012: The schedule has been changed. 17/02/2012: Evaluation script for the scope detection task has been released.
Tasks. Correlación no implica causalidad, hay que decirlo más. A pesar de que es una de las advertencias más repetidas, sobre todo en el ámbito de la ciencia, también constituye uno de los errores o ilusiones cognitivas más frecuentes. Nos referimos a "correlación no implica causalidad" (CINAC, Correlation is not a cause; unas siglas que deberíamos llevar estampadas en la camiseta). En pocas palabras, lo que describe esta advertencia es que si dos hechos se producen al mismo tiempo o parecen estar relacionados entre sí, ello no significa necesariamente que uno de los hechos sea causa del otro. El clásico ejemplo, del que hablamos hace unos días, es la homeopatía y el "pues a mí me funciona": el paciente toma homeopatía, su patología mejora y el paciente infiere que la causa de su mejora ha sido la homeopatía (cuando podría haber sido cualquier otra cosa, como por ejemplo, una simple remisión espontánea).
Gasto de EEUU en ciencia y suicidios Consumo de queso y muerte por enredarse en las sábanas Tasa de divorcios en Maine y consumo de margarina. Natalino Busa.