The cooperative hierarchical structure is a common and significant data structure observed in, or adopted by, many research areas, such as: text mining (author-paper-word) and multi-label classification (label-instance-feature). Renowned Bayesian approaches for cooperative hierarchical structure modeling are mostly based on topic models. However, these approaches suffer from a serious issue in that the number of hidden topics/factors needs to be fixed in advance and an inappropriate number may lead to overfitting or underfitting. One elegant way to resolve this issue is Bayesian nonparametric learning, but existing work in this area still cannot be applied to cooperative hierarchical structure modeling. In this paper, we propose a cooperative hierarchical Dirichlet process (CHDP) to fill this gap. Each node in a cooperative hierarchical structure is assigned a Dirichlet process to model its weights on the infinite hidden factors/topics. Together with measure inheritance from hierarchical Dirichlet process, two kinds of measure cooperation, i.e., superposition and maximization, are defined to capture the many-to-many relationships in the cooperative hierarchical structure. Furthermore, two constructive representations for CHDP, i.e., stick-breaking and international restaurant process, are designed to facilitate the model inference. Experiments on synthetic and real-world data with cooperative hierarchical structures demonstrate the properties and the ability of CHDP for cooperative hierarchical structure modeling and its potential for practical application scenarios.

We present a probabilistic language model for time-stamped text data which tracks the semantic evolution of individual words over time. The model represents words and contexts by latent trajectories in an embedding space. At each moment in time, the embedding vectors are inferred from a probabilistic version of word2vec (Mikolov et al., 2013b). These embedding vectors are connected in time through a latent diffusion process. We describe two scalable variational inference algorithms--skipgram smoothing and skip-gram filtering--that allow us to train the model jointly over all times; thus learning on all data while simultaneously allowing word and context vectors to drift. Experimental results on three different corpora demonstrate that our dynamic model infers word embedding trajectories that are more interpretable and lead to higher predictive likelihoods than competing methods that are based on static models trained separately on time slices.

Linear Mixed Models (LMMs) are important tools in statistical genetics. When used for feature selection, they allow to find a sparse set of genetic traits that best predict a continuous phenotype of interest, while simultaneously correcting for various confounding factors such as age, ethnicity and population structure. Formulated as models for linear regression, LMMs have been restricted to continuous phenotypes. We introduce the Sparse Probit Linear Mixed Model (Probit-LMM), where we generalize the LMM modeling paradigm to binary phenotypes. As a technical challenge, the model no longer possesses a closed-form likelihood function. In this paper, we present a scalable approximate inference algorithm that lets us fit the model to high-dimensional data sets. We show on three real-world examples from different domains that in the setup of binary labels, our algorithm leads to better prediction accuracies and also selects features which show less correlation with the confounding factors.

In the first article of this series, I had touched upon key concepts and processes of Data Science. In this article, I will dive in a bit deeper. First, I will define what is Statistical learning. Then, we will dive into key concepts in Statistical learning. Believe me; it is simple.

So, this is my first NLP/ML project and I would like to share it here. So, using LinearSVM I am trying to classify a question into a category to best describe its intentions.

In this article, we are going to learn how the logistic regression model works in machine learning. The logistic regression model is one member of the supervised classification algorithm family. The building block concepts of logistic regression can be helpful in deep learning while building the neural networks. Logistic regression classifier is more like a linear classifier which uses the calculated logits (score) to predict the target class. If you are not familiar with the concepts of the logits, don't frighten. We are going to learn each and every block of logistic regression by the end of this post.

In this talk we will show how many common use cases use the common algorithms like Logistic Regression, Random Forest, Decision Trees, Clustering, NLP etc. Spark has several Machine Learning algorithms built in and has excellent scalability. We will show how we solved some of the problems of establishing feature vectors, choosing algorithms and then deploying models into production. We will showcase our use of Scala, R and Python to implement models using language of choice yet deploying quickly into production on 500 node Spark clusters.

Almost all organizations now have a need for datascience and as such the main challenge after determining the algorithm is to scale it up and make it operational. In this talk we will show how many common use cases use the common algorithms like Logistic Regression, Random Forest, Decision Trees, Clustering, NLP etc. Spark has several Machine Learning algorithms built in and has excellent scalability. Hence we at comcast built a platform to provide DSaaS on top of Spark with REST API as a means of controlling and submitting jobs so as to abstract most users from the rigor of writing(repeating) code instead focusing on the actual requirements. We will show how we solved some of the problems of establishing feature vectors, choosing algorithms and then deploying models into production. We will showcase our use of Scala, R and Python to implement models using language of choice yet deploying quickly into production on 500 node Spark clusters.

In this post, we'll use a supervised machine learning technique called logistic regression to predict delayed flights. But before we proceed, I like to give condolences to the family of the the victims of the Germanwings tragedy. Note: This is a common data set in the machine learning community to test out algorithms and models given it's publicly available and have sizable data. In this blog, we will look at small sample snapsot(2201 flights in January 2004). In another post, we can explore using Big Data technologies such as Hadoop MapReduce or Spark machine learning libraries to do large scale predictive analytics and data mining.

Let me know what you think @StephLKim.