Traditional graph-based semi-supervised learning (SSL) approaches, even though widely applied, are not suited for massive data and large label scenarios since they scale linearly with the number of edges $|E|$ and distinct labels $m$. To deal with the large label size problem, recent works propose sketch-based methods to approximate the distribution on labels per node thereby achieving a space reduction from $O(m)$ to $O(\log m)$, under certain conditions. In this paper, we present a novel streaming graph-based SSL approximation that captures the sparsity of the label distribution and ensures the algorithm propagates labels accurately, and further reduces the space complexity per node to $O(1)$. We also provide a distributed version of the algorithm that scales well to large data sizes. Experiments on real-world datasets demonstrate that the new method achieves better performance than existing state-of-the-art algorithms with significant reduction in memory footprint. We also study different graph construction mechanisms for natural language applications and propose a robust graph augmentation strategy trained using state-of-the-art unsupervised deep learning architectures that yields further significant quality gains.

When the training data in a two-class classification problem is overwhelmed by one class, most classification techniques fail to correctly identify the data points belonging to the underrepresented class. We propose Similarity-based Imbalanced Classification (SBIC) that learns patterns in the training data based on an empirical similarity function. To take the imbalanced structure of the training data into account, SBIC utilizes the concept of absent data, i.e. data from the minority class which can help better find the boundary between the two classes. SBIC simultaneously optimizes the weights of the empirical similarity function and finds the locations of absent data points. As such, SBIC uses an embedded mechanism for synthetic data generation which does not modify the training dataset, but alters the algorithm to suit imbalanced datasets. Therefore, SBIC uses the ideas of both major schools of thoughts in imbalanced classification: Like cost-sensitive approaches SBIC operates on an algorithm level to handle imbalanced structures; and similar to synthetic data generation approaches, it utilizes the properties of unobserved data points from the minority class. The application of SBIC to imbalanced datasets suggests it is comparable to, and in some cases outperforms, other commonly used classification techniques for imbalanced datasets.

This paper reports on an ongoing effort to provide computational linguistic support to scholars making use of the writings from the Third Dynasty of Ur, especially those trying to link reports of financial transactions together for the purpose of social networking. The computational experiments presented are especially addressed to the problem of identifying proper names for the ultimate purpose of reconstructing a social network of UR III society. We describe the application of established supervised learning algorithms, compare its results to previous work using unsupervised methods and propose future work based upon these comparative results.

Is it possible to classify and predict (yes, predict!) if market trends will be bullish, bear or ranged by using a method called "naïve" and based on something as simple as Bayes' theorem is? Let's see! Our main objective is to explore techniques of machine learning that can help us not only to label series in a posteriori analysis, but also to predict to which class a new value given of the serie belongs to. The Naïve Bayesian Classifier is a supervised learning method of machine learning as well as a statistical method for classification. Although this method is including in its name a word as rare as "naïve" is, it will be our tool chosen to predict different trends of a market represented by an index. Bayesian classification provides practical learning algorithms where prior knowledge and observed data can be combined.

We develop a framework to solve machine learning problems in cases where latent geometric structure in the feature space may be exploited. We consider cases where the number of training examples is either very large, or signals are sequentially observed by a platform operating in real-time such as an autonomous robot. In the former case, since the sample size is large-scale, processing a few training examples at a time is necessary due to computational cost. However, doing so at a centralized location may be impractical, which motivates the use of learning techniques that may be done collaboratively by a network of interconnected computing servers. In the later case, an autonomous robot with no priors on its operating environment only has access to information based on the path it has traversed, which may omit regions of the feature space crucial for tasks such as learning-based control. By communicating with other robots in a network, individuals may learn over a broader domain associated with that which has been explored by the whole network, and thus more effectively solve autonomous learning tasks.

A fearless Frenchman, Franky Zapata, thinks one day people will be able to ride his hoverboard to pick up bread in the morning (it's a French thing). The jet ski champion on Saturday set a new Guinness World Record for the farthest hoverboard flight – yes, just like in the movies – off the coast of Sausset-les-Pins in the south of France. Mr. Zapata rode the 1,000 horsepower drone, standing on top of it, for 7,388 feet, or more than a mile. He hovered 165 feet above the surface of the water, "trailed by a fleet of boats and jet skis," as Guinness reports. His feat shattered the previous hoverboard travel record of 905 feet and 2 inches, set last year by Canadian inventor Catalin Alexandru Duru.

I want to play around with twitter sentiment analysis and my main hesitation is on obtaining a dataset that's been classified for supervised learning. Do you know if a decent one exists already, or would I have to create one myself (or use mechical turk)?

Though there are many possible supervised learning model types to choose from, gradient boosted models (GBMs) are almost always my first choice. In many cases, they end up outperforming other options, and even when they don't, it's rare that a properly tuned GBM is far behind the best model. At a high level, the way GBMs work is by starting with a rough prediction and then building a series of decision trees, with each tree in the series trying to correct the prediction error of the tree before it. There's more detailed descriptions of the mechanics behind the algorithm out there, but this series of posts is intended to give more of an intuitive understanding of what the algorithm does. For this series, I'll be using a synthetic 2-dimensional classification dataset generated using scikit-learn's make_classification().

Structured prediction is used in areas such as computer vision and natural language processing to predict structured outputs such as segmentations or parse trees. In these settings, prediction is performed by MAP inference or, equivalently, by solving an integer linear program. Because of the complex scoring functions required to obtain accurate predictions, both learning and inference typically require the use of approximate solvers. We propose a theoretical explanation to the striking observation that approximations based on linear programming (LP) relaxations are often tight on real-world instances. In particular, we show that learning with LP relaxed inference encourages integrality of training instances, and that tightness generalizes from train to test data.

Boosting is an ensemble technique that attempts to create a strong classifier from a number of weak classifiers. In this post you will discover the AdaBoost Ensemble method for machine learning. This post was written for developers and assumes no background in statistics or mathematics. The post focuses on how the algorithm works and how to use it for predictive modeling problems. If you have any questions, leave a comment and I will do my best to answer.