Analyzing and Interpreting Convolutional Neural Networks in NLP

Machine Learning Models and Tools (MachineShop)Meta-package for statistical and machine learning with a common interface for model fitting, prediction, performance assessment, and presentation of re …

Learning Automata Based SVM (LA-SVM) As an indispensable defensive measure of network security, the intrusion detection is a process of monitoring the events occurring in a computer system or network and analyzing them for signs of possible incidents. It is a classifier to judge the event is normal or malicious. The information used for intrusion detection contains some redundant features which would increase the difficulty of training the classifier for intrusion detection and increase the time of making predictions. To simplify the training process and improve the efficiency of the classifier, it is necessary to remove these dispensable features. in this paper, we propose a novel LA-SVM scheme to automatically remove redundant features focusing on intrusion detection. This is the first application of learning automata for solving dimension reduction problems. The simulation results indicate that the LA-SVM scheme achieves a higher accuracy and is more efficient in making predictions compared with traditional SVM. …

Every new study presents its own challenges. (I would have to say that one of the great things about being a biostatistician is the immense variety of research questions that I get to wrestle with.) Recently, I was approached by a group of researchers who wanted to evaluate an intervention. Actually, they had two, but the second one was a minor tweak added to the first. They were trying to figure out how to design the study to answer two questions: (1) is intervention (A) better than doing nothing and (2) is (A^+), the slightly augmented version of (A), better than just (A)?

Rough Concept Analysis The theory introduced, presented and developed in this paper, is concerned with Rough Concept Analysis. This theory is a synthesis of the theory of Rough Sets pioneered by Zdzislaw Pawlak with the theory of Formal Concept Analysis pioneered by Rudolf Wille. The central notion in this paper of a rough formal concept combines in a natural fashion the notion of a rough set with the notion of a formal concept: ‘rough set + formal concept = rough formal concept’. A follow-up paper will provide a synthesis of the two important data modeling techniques: conceptual scaling of Formal Concept Analysis and Entity-Relationship database modeling. …

An Instance Transfer based Approach Using Enhanced Recurrent Neural Network for Domain Named Entity Recognition

Testing your R package is crucial, and thankfully it only gets easier with time, thanks to experience… and awesome packages helping you setup and improve tests! In this post, we shall offer a roundup of packages for testing R packages, first in a section about general testing setup, and then in a section about testing “peculiar” stuff.

Accurat, in partnership with the Google News Initiative, built an augmented reality app to build statues of hope:

A Parallel Random Forest Algorithm for Big Data in a Spark Cloud Computing Environment

Amazon SageMaker now supports running Batch Transform jobs in Amazon Virtual Private Cloud (Amazon VPC) and using AWS Key Management Service (AWS KMS). Amazon VPC allows you to control access to your machine learning (ML) model containers and data so that they are private and aren’t accessible over the internet. AWS KMS enables you to encrypt data on the storage volume attached to the ML compute instances that run the Batch Transform job. This ensures that the model artifacts, logs, and temporary files used in your Batch Transform jobs are always secure. In this blog, I show you how to apply these features to Batch Transform jobs.