Clustering is an important problem which is prevalent in a variety of real world problems. One of the first and widely applied clustering algorithms is k-means, which was named by James MacQueen [15], but was proposed by Hugo Steinhaus [23] even before. Despite being half a century old, k-means has been widely used and analyzed under various settings. One major drawback of k-means is its incapability to separate clusters that are non-linearly separated. This can be alleviated by mapping the data to a high dimensional feature space and do clustering on top of the feature space [21, 9, 12], which is generally called kernel-based methods. For instance, the widely-used spectral clustering [22, 17] is an algorithm to calculate top eigenvectors of a kernel matrix of affinities, followed by a k-means on the top r eigenvectors. The consistency of spectral clustering is analyzed by [25].

We present a general theoretical analysis of structured prediction with a series of new results. We give new data-dependent margin guarantees for structured prediction for a very wide family of loss functions and a general family of hypotheses, with an arbitrary factor graph decomposition. These are the tightest margin bounds known for both standard multi-class and general structured prediction problems. Our guarantees are expressed in terms of a data-dependent complexity measure, factor graph complexity, which we show can be estimated from data and bounded in terms of familiar quantities. We further extend our theory by leveraging the principle of Voted Risk Minimization (VRM) and show that learning is possible even with complex factor graphs. We present new learning bounds for this advanced setting, which we use to design two new algorithms, Voted Conditional Random Field (VCRF) and Voted Structured Boosting (StructBoost). These algorithms can make use of complex features and factor graphs and yet benefit from favorable learning guarantees. We also report the results of experiments with VCRF on several datasets to validate our theory.

We consider the problem of learning in single-player and multiplayer multiarmed bandit models. Bandit problems are classes of online learning problems that capture exploration versus exploitation tradeoffs. In a multiarmed bandit model, players can pick among many arms, and each play of an arm generates an i.i.d. reward from an unknown distribution. The objective is to design a policy that maximizes the expected reward over a time horizon for a single player setting and the sum of expected rewards for the multiplayer setting. In the multiplayer setting, arms may give different rewards to different players. There is no separate channel for coordination among the players. Any attempt at communication is costly and adds to regret. We propose two decentralizable policies, $\tt E^3$ ($\tt E$-$\tt cubed$) and $\tt E^3$-$\tt TS$, that can be used in both single player and multiplayer settings. These policies are shown to yield expected regret that grows at most as O($\log^{1+\epsilon} T$). It is well known that $\log T$ is the lower bound on the rate of growth of regret even in a centralized case. The proposed algorithms improve on prior work where regret grew at O($\log^2 T$). More fundamentally, these policies address the question of additional cost incurred in decentralized online learning, suggesting that there is at most an $\epsilon$-factor cost in terms of order of regret. This solves a problem of relevance in many domains and had been open for a while.

Nintendo Reports Second Quarter Losses But 3DS Sales Are Up Thanks To'Pokmon GO' We've all been there – scouring the internet for items of clothing to buy to suit any given occasion, only to feel more than overwhelmed by the volume of choice now available and increasingly frustrated by how hard it is to actually find something we like. Abigail Holtz is out there to try and solve that problem. Hailing from Google where she worked on a multitude of different shopping products, including Boutiques.com The vision is to create a'Pandora for fashion' – a site that uses recommendation technology (based on what Holtz calls a "fashion genome" and her "secret sauce") as well as human input, to suggest looks for shoppers informed by things like style, fit, body shape, even occasion appropriateness. It's got an intuitive UI/UX built around a simple like or dislike (heart or cross) functionality, and a straightforward breakdown of clothing categories based on trends as well as occasions.

The meeting format typically alternates between lecture/paper discussions and lab sessions where we review code. In our lecture sessions we discuss and gain a better understanding of course lectures. In our lab sessions, we walk methodically through code from course assignments. We intend to expand our projects beyond the course material, based on the interests of the group. We welcome all new members and participants, regardless of experience level, who are excited about rolling up their sleeves to dig into Deep Learning.

In machine learning, you may often wish to build predictors that allows to classify things into categories based on some set of associated values. For example, it is possible to provide a diagnosis to a patient based on data from previous patients. Many algorithms have been developed for automated classification, and common ones include random forests, support vector machines, Naïve Bayes classifiers, and many types of neural networks. To get a feel for how classification works, we take a simple example of a classification algorithm – k-Nearest Neighbours (kNN) – and build it from scratch in Python 2. You can use a mostly imperative style of coding, rather than a declarative/functional one with lambda functions and list comprehensions to keep things simple if you are starting with Python. Here, we will provide an introduction to the latter approach.

There are many Python machine learning resources freely available online. Go from zero to Python machine learning hero in 7 steps! The first step is often the hardest to take, and when given too much choice in terms of direction it can often be debilitating. This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way. The prime objective of this outline is to help you wade through the numerous free options that are available; there are many, to be sure, but which are the best?

The Perceptron algorithm is the simplest type of artificial neural network. It is a model of a single neuron that can be used for two-class classification problems and provides the foundation for later developing much larger networks. In this tutorial, you will discover how to implement the Perceptron algorithm from scratch with Python. How to train the network weights for the Perceptron. How to implement the Perceptron algorithm for a real-world classification problem.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more.

The'smart' speakers that won't stop talking to each other: Watch Amazon's Echo Dot get stuck in an'infinite loop' chatting to Google's Home Google's $130 Home speaker went on sale earlier this month Amazon's Alexa has been a huge hit with 5.1m sold Both can do everything from control lights to answer questions Google's $130 Home speaker went on sale earlier this month Amazon's Alexa has been a huge hit with 5.1m sold Has YOUR Google account been hacked? Researchers say... Apple goes Red for World AIDS day as firm is revealed to... Britain traded with the Middle East 1,300 years ago: Bitumen... The original human ancestor'Lucy' was a tree climbing... Has YOUR Google account been hacked? Researchers say... Apple goes Red for World AIDS day as firm is revealed to... Britain traded with the Middle East 1,300 years ago: Bitumen... The original human ancestor'Lucy' was a tree climbing... Google Home AI speaker (left) shows the incredible potential of a smart home assistant - but still has a little bit of learning to do before it become indispensable.