Support Vector Data Description (SVDD) is a popular outlier detection technique which constructs a flexible description of the input data. SVDD computation time is high for large training datasets which limits its use in big-data process-monitoring applications. We propose a new iterative sampling-based method for SVDD training. The method incrementally learns the training data description at each iteration by computing SVDD on an independent random sample selected with replacement from the training data set. The experimental results indicate that the proposed method is extremely fast and provides a good data description .

K-nearest neighbors, or KNN, is a supervised learning algorithm for either classification or regression. It's super intuitive and has been applied to many types of problems. To make a personalized offer to one customer, you might employ KNN to find similar customers and base your offer on their purchase behaviors. KNN has also been applied to medical diagnosis and credit scoring. This is a post about the K-nearest neighbors algorithm and Python.

Parallel Bayesian MCMC Imputation for Multiple Distributed Lag Models: A Case Study in Environmental Epidemiology by Brian Caffo, Roger Peng, Francesca Dominici, Thomas Louis and Scott Zeger.

I am currently working on a project that uses LSTMs/GRUs to predict the a time series as described in here(LSTM For Regression Using the Window Method) and here. Does anybody have scientific sources for this method by any chance? I can not seem to find any paper that uses this approach and I am hesitant to use it because of that. Here is a paper that mentiones the sliding time window approach (but not for LSTMs). Why do you need a paper?

In this letter, a Hierarchical Parametric Empirical Bayes model is proposed to model spike count data. We have integrated Generalized Linear Models (GLMs) and empirical Bayes theory to simultaneously provide three advantages: (1) a model of over-dispersion of spike count values; (2) reduced MSE in estimation when compared to using the maximum likelihood method for GLMs; and (3) an efficient alternative to inference with fully Bayes estimators. We apply the model to study both simulated data and experimental neural data from the retina. The simulation results indicate that the new model can estimate both the weights of connections among neural populations and the output firing rates (mean spike count) efficiently and accurately. The results from the retinal datasets show that the proposed model outperforms both standard Poisson and Negative Binomial GLMs in terms of the prediction log-likelihood of held-out datasets.

For my Insight Data Science project, I built a linear regression model to predict nightly tap room attendance for New Republic Brewing Co. Using this model, New Republic will be able to schedule the appropriate number of bartenders for any given night. When it came time to decide how I would deliver this model to New Republic, I decided to eschew the typical dashboard in favor of building a Slack bot. New Republic had recently switched from communicating through Google Hangouts to Slack because they wanted to take advantage of Slack's integrations, so I took this opportunity to explore Slack integration creation. Building a Slack bot to interact with the model has many advantages. It provides a user friendly interface to the model that allows anyone at New Republic to interact with it.

I am a data mining trainer and consultant. This book not only has good content, but it offers a 90 day license of software with which to rehearse the case study examples. My comments on the book will be accompanied by comments on the software. The book is the perfect fit for its intended audience. With the caution that certain readers will do better elsewhere, I think it is a great book.

For a data scientist is essential to be familiar with the most important and current fields of research in machine learning and data mining. The algorithms in machine learning and data mining advance to a higher level of accuracy and flexibility and a data scientist should be prepared to implement the best algorithms and methods. The investigation of most common topics in machine learning and data mining provides an insight about the most relevant areas of research. To achieve this goal, I used the database of ScienceDirect.com. ScienceDirect has access to about 2,500 academic journals, more than 26,000 e-books and more than 13 million articles.

In supervised learning, the learning algorithm is provided outcome data in advance, in the form of a pre-labeled set of instances. It is from this set that the algorithm is expected to learn what to do when it encounters future, previously unseen instances. Classification is a form of supervised learning. As an example, take the biological taxonomic hierarchy. Organisms are grouped into successfully more specific ranks of domain, kingdom, phylum, etc.

In part one, we showed how the machine learning process is like the scientific thinking process, and in part two, we introduced a benchmark task and showed how to get your machine learning system up and running with a simple nearest neighbors model. Time to crank it up! In this post, we continue the scientific thinking process by extending our simple starting model into increasingly powerful models. We'll show how to implement a particularly useful kind of model in Tensorflow. Shortcutting this process is a common failure mode--ye've been warned! But familiarity with common patterns of errors, diagnoses, and solutions allow experts to zip through the iterations. From the perspective of someone who is learning, it might look like the expert is skipping steps and going straight to the complicated stuff.