Blog Archives

Operations Research with R

June 23, 2017
By

Stefan Feuerriegel This blog entry concerns our course on “Operations Reserch with R” that we teach as part of our study program. We hope that the materials are of value to lectures and everyone else working in the field of numerical optimiatzion. Course outline The course starts with a review of numerical and linear algebra … Continue reading "Operations...

Read more »

Package “SentimentAnalysis” released on CRAN

June 6, 2017
By

Authors: Stefan Feuerriegel, Nicolas Pröllochs This report introduces sentiment analysis in R and shows how to use our package “SentimentAnalysis”. What is sentiment analysis? Sentiment analysis is a research branch located at the heart of natural language processing (NLP), computational linguistics and text mining. It refers to any measures by which subjective information is extracted … Continue reading "Package...

Read more »

ReinforcementLearning: A package for replicating human behavior in R

April 8, 2017
By
ReinforcementLearning: A package for replicating human behavior in R

Nicolas Proellochs and Stefan Feuerriegel 2017-04-06 Introduction Reinforcement learning has recently gained a great deal of traction in studies that call for human-like learning. In settings where an explicit teacher is not available, this method teaches an agent via interaction with its environment without any supervision other than its own decision-making policy. In many cases, … Continue reading "ReinforcementLearning:...

Read more »

Unit Testing in R

March 14, 2017
By

Software testing describes several means to investigate program code regarding its quality. The underlying approaches provides means to handle errors once they occur. Furthermore, software testing also show techniques to reduce the probability of that. R is becoming a increasingly promiment programming language. This not only includes pure statistical settings but also machine learning, dashboards … Continue reading "Unit...

Read more »

Ensemble Learning in R

March 7, 2017
By

Previous research in data mining has devised numerous different algorithms for learning tasks. While an individual algorithm might already work decently, one can usually obtain a better predictive by combining several. This approach is referred to as ensemble learning. Common examples include random forests, boosting and AdaBost in particular. Our slide deck is positioned at … Continue reading "Ensemble...

Read more »

Reinforcement Learning in R

February 27, 2017
By

Reinforcement learning has gained considerable traction as it mines real experiences with the help of trial-and-error learning to model decision-making. Thus, this approach attempts to imitate the fundamental method used by humans of learning optimal behavior without the requirement of an explicit model of the environment. In contrast to many other approaches from the domain … Continue reading "Reinforcement...

Read more »

Sentiment Analysis in R

February 21, 2017
By

Current research in finance and the social sciences utilizes sentiment analysis to understand human decisions in response to textual materials. While sentiment analysis has received great traction lately, the available tools are not yet living up to the needs of researchers. Especially R has not yet capabilities that most research desires. Our package “SentimentAnalysis” performs … Continue reading "Sentiment...

Read more »

Optimization and Operations Research in R

February 17, 2017
By

Authors: Stefan Feuerriegel and Joscha Märkle-Huß R is widely taught in business courses and, hence, known by most data scientists with business background. However, when it comes to optimization and Operations Research, many other languages are used. Especially for optimization, solutions range from Microsoft Excel solvers to modeling environments such as Matlab and GAMS. Most … Continue reading "Optimization...

Read more »

caffeR: an R wrapper for ‘caffe’

February 11, 2017
By

Authors: Christof Naumzik & Stefan Feuerriegel Caffe (http://caffe.berkeleyvision.org) provides a powerful framework for deep learning. It is developed and maintained by the Berkeley Vision and Learning Center (BVLC) and has received a great deal of traction lately. Caffe enables users to define and train custom-made neural networks without hard-coding. Furthermore, it allows users to execute … Continue reading "caffeR:...

Read more »

Deep Learning in R

February 6, 2017
By
Deep Learning in R

  Oksana Kutkina, Stefan Feuerriegel March 7, 2016 Introduction Deep learning is a recent trend in machine learning that models highly non-linear representations of data. In the past years, deep learning has gained a tremendous momentum and prevalence for a variety of applications (Wikipedia 2016a). Among these are image and speech recognition, driverless cars, natural … Continue reading "Deep...

Read more »

Search R-bloggers


Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)