Boosting
Boosting is a machine learning meta-algorithm for reducing bias in supervised learning. Boosting is based on the question posed by Kearns: Can a set of weak learners create a single strong learner? A weak learner is defined to be a classifier which is only slightly correlated with the true classification (it can label examples better than random guessing). In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification.
Schapire’s affirmative answer to Kearns’ question has had significant ramifications in machine learning and statistics, most notably leading to the development of boosting.
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Geometric Mean Metric Learning
We revisit the task of learning a Euclidean metric from data. We approach this problem from first principles and formulate it as a surprisingly simple optimization problem. Indeed, our formulation even admits a closed form solution. This solution possesses several very attractive properties: (i) an innate geometric appeal through the Riemannian geometry of positive definite matrices; (ii) ease of interpretability; and (iii) computational speed several orders of magnitude faster than the widely used LMNN and ITML methods. Furthermore, on standard benchmark datasets, our closed-form solution consistently attains higher classification accuracy. …

Docker
Build, Ship and RunAny App, Anywhere. Docker – An open platform for distributed applications for developers and sysadmins.
Docker is a relatively new open source application and service, which is seeing interest across a number of areas. It uses recent Linux kernel features (containers, namespaces) to shield processes. While its use (superficially) resembles that of virtual machines, it is much more lightweight as it operates at the level of a single process (rather than an emulation of an entire OS layer). This also allows it to start almost instantly, require very little resources and hence permits an order of magnitude more deployments per host than a virtual machine.
Docker offers a standard interface to creation, distribution and deployment. The shipping container analogy is apt: just how shipping containers (via their standard size and “interface”) allow global trade to prosper, Docker is aiming for nothing less for deployment. A Dockerfile provides a concise, extensible, and executable description of the computational environment. Docker software then builds a Docker image from the Dockerfile. Docker images are analogous to virtual machine images, but smaller and built in discrete, extensible and reuseable layers. Images can be distributed and run on any machine that has Docker software installed—including Windows, OS X and of course Linux. Running instances are called Docker containers. A single machine can run hundreds of such containers, including multiple containers running the same image.
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