This website is an ongoing project to develop a comprehensive repository for research into time series classification. If you use the results or code, please cite the paper ‘Anthony Bagnall, Jason Lines, Aaron Bostrom, James Large and Eamonn Keogh, The Great Time Series Classification Bake Off: a Review and Experimental Evaluation of Recent Algorithmic Advances, Data Mining and Knowledge Discovery, 31(3), 2017’.
In many cases, robots will need to move in an environment in order to execute a task. How well they perform this task, however, can strongly depend on the route that they take. For example, a driverless car could take a route with the least amount of traffic in order to minimise travelling time. Or, it could choose a route with the shortest travelling distance to minimise fuel consumption. Symbolic planning investigates how robots can choose the best route based on the task and the constraint on accomplishing that task (such as least travelling time or shortest travelling distance).
Planning is a long-standing sub-area of Artificial Intelligence (AI). Planning is the task of finding a procedural course of action for a declaratively described system to reach its goals while optimizing overall performance measures. Automated planners find the transformations to apply in each given state out of the possible transformations for that state. In contrast to the classification problem, planners provide guarantees on the solution quality.
RProtoBuf provides R with bindings for the Google Protocol Buffers (‘ProtoBuf’) data encoding and serialization library used and released by Google, and deployed fairly widely in numerous projects as a language and operating-system agnostic protocol.
In the modern world, with great advancement of Computer Science and rapidly growing silicon industry, stocks are one of the major assets people are counting upon. Majority of people, with sound knowledge of the market, statistics and lot of ‘gut’ feelings, are investing their hard earned money into company shares. And then we have people, who are termed as ‘Risk Takers’, who believe in the understanding of commerce, current affairs, and mathematics. They are the major players in the world of intra-day trading (One can categorize it to be one of the riskiest investment in stocks market, quite equivalent to gambling).
We all know about the Classical Machine Learning Classification Algorithm, K-Nearest Neighbour as one of the most successful non-parametric Machine Learning Algorithm. It was first introduced by Fix and Hodges in an unpublished US Air Force School of Aviation Medicine report on 1951 , that came to be known as k-nearest neighbour rule and underwent further modifications in succeeding years. In recent times, it is well known to work fine on Image Classification problems related to Computer Vision.
With the growing popularity of Data Science, many buzzwords have been loosely thrown around without a proper understanding of what they truly mean. Some of these buzzwords include terms like Data Analytics, Big Data, Artificial Intelligence and Machine Learning. But unlike the terms mentioned in the posts on ANCOVA, Moderation and The Confusion Matrix, many of these terms in Data Science are not actually interchangeable. This post attempts to explain the subtle differences of these buzzwords, so that we can all speak a common language that is less confusing.
Data science today is a hot topic with companies talking about being ‘Data Driven’, ‘Data informed’, or ‘Data Centric’ and the changes they have made in their approach to a new business model because of this, and why not? The impact of using data to make more informed (?) decisions to a problem has seen businesses thrive. Naively at lot of businesses think all it takes is to hit the big red button marked ‘Data Science’ get the answer of 42 and sit back and watch the $$$’s roll in, if you’ve been in this world you know this isn’t the case. This article is not about the strategic business process of transforming a company to be data driven, or on the difficulties you will almost certainly encounter along the way (FYI This will mainly be about educating your audience, dealing with hostility to change or egos). This article is focused on the ground up approach to building the data infrastructure needed to support your data scientist needs.
Google has now added machine learning (ML) capabilities to its Google BigQuery, the company’s petabyte (PB)-scale cloud database offering. Now dubbed BigQuery ML, the new version lets you use simple Structured Query Language (SQL) statements to build and deploy ML models for predictive analytics.
We demonstrate MLOG, a high-level language that integrates machine learning into data management systems. Unlike existing machine learning frameworks (e.g., TensorFlow, Theano, and Caffe), MLOG is declarative, in the sense that the system manages all data movement, data persistency, and machine-learning related optimizations (such as data batching) automatically. Our interactive demonstration will show audience how this is achieved based on the novel notion of tensoral views (TViews), which are similar to relational views but operate over tensors with linear algebra. With MLOG, users can succinctly specify not only simple models such as SVM (in just two lines), but also sophisticated deep learning models that are not supported by existing in-database analytics systems (e.g., MADlib, PAL, and SciDB), as a series of cascaded TViews. Given the declarative nature of MLOG, we further demonstrate how query/program optimization techniques can be leveraged to translate MLOG programs into native TensorFlow programs. The performance of the automatically generated Tensor- Flow programs is comparable to that of hand-optimized ones.
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