I came across a book titled Calculus of Thought: Neuromorphic Logistic Regression in Cognitive Machines. It introduces a method called reduced error logistic regression (RELR). Does anyone know anything about it? Yes, but I thought it was better to ask before wasting one week to understand and reproduce the results like I did with Numenta's HTM.

This topic describes how to load machine learning (ML) models that have been built using Spark MLlib and stored in Azure Blob Storage (WASB), and how to score them with datasets that have also been stored in WASB. It shows how to pre-process the input data, transform features using the indexing and encoding functions in the MLlib toolkit, and how to create a labeled point data object that can be used as input for scoring with the ML models. The models used for scoring include Linear Regression, Logistic Regression, Random Forest Models, and Gradient Boosting Tree Models. You need an Azure account and an HDInsight Spark cluster to begin this walkthrough. See the Overview of Data Science using Spark on Azure HDInsight for these requirements, for a description of the NYC 2013 Taxi data used here, and for instructions on how execute code from a Jupyter notebook on the Spark cluster.

This paper investigates the problem of cross-lingual transfer parsing, aiming at inducing dependency parsers for low-resource languages while using only training data from a resource-rich language (e.g., English). Existing model transfer approaches typically don't include lexical features, which are not transferable across languages. In this paper, we bridge the lexical feature gap by using distributed feature representations and their composition. We provide two algorithms for inducing cross-lingual distributed representations of words, which map vocabularies from two different languages into a common vector space. Consequently, both lexical features and non-lexical features can be used in our model for cross-lingual transfer. Furthermore, our framework is flexible enough to incorporate additional useful features such as cross-lingual word clusters. Our combined contributions achieve an average relative error reduction of 10.9% in labeled attachment score as compared with the delexicalized parser, trained on English universal treebank and transferred to three other languages. It also significantly outperforms state-of-the-art delexicalized models augmented with projected cluster features on identical data. Finally, we demonstrate that our models can be further boosted with minimal supervision (e.g., 100 annotated sentences) from target languages, which is of great significance for practical usage.

We study high-dimensional distribution learning in an agnostic setting where an adversary is allowed to arbitrarily corrupt an $\varepsilon$-fraction of the samples. Such questions have a rich history spanning statistics, machine learning and theoretical computer science. Even in the most basic settings, the only known approaches are either computationally inefficient or lose dimension-dependent factors in their error guarantees. This raises the following question:Is high-dimensional agnostic distribution learning even possible, algorithmically? In this work, we obtain the first computationally efficient algorithms with dimension-independent error guarantees for agnostically learning several fundamental classes of high-dimensional distributions: (1) a single Gaussian, (2) a product distribution on the hypercube, (3) mixtures of two product distributions (under a natural balancedness condition), and (4) mixtures of spherical Gaussians. Our algorithms achieve error that is independent of the dimension, and in many cases scales nearly-linearly with the fraction of adversarially corrupted samples. Moreover, we develop a general recipe for detecting and correcting corruptions in high-dimensions, that may be applicable to many other problems.

We propose to model the fixation locations of the human eye when observing a still image by a Markovian point process in R 2 . Our approach is data driven using k-means clustering of the fixation locations to identify distinct salient regions of the image, which in turn correspond to the states of our Markov chain. Bayes factors are computed as model selection criterion to determine the number of clusters. Furthermore, we demonstrate that the behaviour of the human eye differs from this model when colour information is removed from the given image.

The detection of rare variants is important for understanding the genetic heterogeneity in mixed samples. Recently, next-generation sequencing (NGS) technologies have enabled the identification of single nucleotide variants (SNVs) in mixed samples with high resolution. Yet, the noise inherent in the biological processes involved in next-generation sequencing necessitates the use of statistical methods to identify true rare variants. We propose a novel Bayesian statistical model and a variational expectation-maximization (EM) algorithm to estimate non-reference allele frequency (NRAF) and identify SNVs in heterogeneous cell populations. We demonstrate that our variational EM algorithm has comparable sensitivity and specificity compared with a Markov Chain Monte Carlo (MCMC) sampling inference algorithm, and is more computationally efficient on tests of low coverage ($27\times$ and $298\times$) data. Furthermore, we show that our model with a variational EM inference algorithm has higher specificity than many state-of-the-art algorithms. In an analysis of a directed evolution longitudinal yeast data set, we are able to identify a time-series trend in non-reference allele frequency and detect novel variants that have not yet been reported. Our model also detects the emergence of a beneficial variant earlier than was previously shown, and a pair of concomitant variants.

A number of organizations ranging from terrorist groups such as ISIS to politicians and nation states reportedly conduct explicit campaigns to influence opinion on social media, posing a risk to democratic processes. There is thus a growing need to identify and eliminate "influence bots" - realistic, automated identities that illicitly shape discussion on sites like Twitter and Facebook - before they get too influential. Spurred by such events, DARPA held a 4-week competition in February/March 2015 in which multiple teams supported by the DARPA Social Media in Strategic Communications program competed to identify a set of previously identified "influence bots" serving as ground truth on a specific topic within Twitter. Past work regarding influence bots often has difficulty supporting claims about accuracy, since there is limited ground truth (though some exceptions do exist [3,7]). However, with the exception of [3], no past work has looked specifically at identifying influence bots on a specific topic. This paper describes the DARPA Challenge and describes the methods used by the three top-ranked teams.

This article discusses a far more general version of the technique described in our article The best kept secret about regression. Here we adapt our methodology so that it applies to data sets with a more complex structure, in particular with highly correlated independent variables. Our goal is to produce a regression tool that can be used as a black box, be very robust and parameter-free, and usable and easy-to-interpret by non-statisticians. It is part of a bigger project: automating many fundamental data science tasks, to make it easy, scalable and cheap for data consumers, not just for data experts. Readers are invited to further formalize the technology outlined here, and challenge my proposed methodology.

Support Vector Machines are perhaps one of the most popular and talked about machine learning algorithms. They were extremely popular around the time they were developed in the 1990s and continue to be the go-to method for a high-performing algorithm with little tuning. In this post you will discover the Support Vector Machine (SVM) machine learning algorithm. SVM is an exciting algorithm and the concepts are relatively simple. This post was written for developers with little or no background in statistics and linear algebra.

Of course we have all heard about machine learning and recommendation engines in big business ecommerce. For quite some time, massive ecommerce businesses like Netflix, Amazon, and Ebay have been leveraging the power of data science to improve customer service and boost sales. Where once this technology was cost-prohibitive to all but the major players, recently things have changed. Thanks to multi-channel ecommerce platforms like Shopify, and the developers who are building custom machine learning add-ons, now mom and pop online businesses get the chance to infuse their operations with the power of data science. In this article I introduce how machine learning algorithms work to produce recommendation systems for small business ecommerce.