Topic models for text corpora comprise a popular family of methods that have inspired many extensions to encode properties such as sparsity, interactions with covariates, and the gradual evolution of topics. In this paper, we combine certain motivating ideas behind variations on topic models with modern techniques for variational inference to produce a flexible framework for topic modeling that allows for rapid exploration of different models. We first discuss how our framework relates to existing models, and then demonstrate that it achieves strong performance, with the introduction of sparsity controlling the trade off between perplexity and topic coherence.

Background-Foreground classification is a fundamental well-studied problem in computer vision. Due to the pixel-wise nature of modeling and processing in the algorithm, it is usually difficult to satisfy real-time constraints. There is a trade-off between the speed (because of model complexity) and accuracy. Inspired by the rejection cascade of Viola-Jones classifier, we decompose the Gaussian Mixture Model (GMM) into an adaptive cascade of classifiers. This way we achieve a good improvement in speed without compensating for accuracy. In the training phase, we learn multiple KDEs for different durations to be used as strong prior distribution and detect probable oscillating pixels which usually results in misclassifications. We propose a confidence measure for the classifier based on temporal consistency and the prior distribution. The confidence measure thus derived is used to adapt the learning rate and the thresholds of the model, to improve accuracy. The confidence measure is also employed to perform temporal and spatial sampling in a principled way. We demonstrate a speed-up factor of 5x to 10x and 17 percent average improvement in accuracy over several standard videos.

When I first started out learning about machine learning algorithms, it turned out to be quite a task to gain an intuition of what the algorithms are doing. Not just because it was difficult to understand all the mathematical theory and notations, but it was also plain boring. When I turned to online tutorials for answers, I could again only see equations or high level explanations without going through the detail in a majority of the cases. It was then that one of my data science colleagues introduced me to the concept of working out an algorithm in an excel sheet. And that worked wonders for me.

"The road to machine learning starts with Regression. Running a regression model is a no-brainer. Once you are finished reading this article, you'll able to build, improve, and optimize regression models on your own. Note: This article is best suited for people new to machine learning with requisite knowledge of statistics.

For the past couple of months, I have been reading and doing a lot of stuff related to machine learning and sports. I have not come up with something that has good accuracy until today. A friend suggested me to predict the projected scores since the projected scores shown on the screen during a cricket match are not very accurate. This post shows that a simple linear regression model can outperform the traditional methods and the accuracy it gives is very good. Right now, the projected scores we get on the screen during a match are not very interesting to people watching cricket.

This post will be about two methods that slightly modify ordinary least squares (OLS) regression – ridge regression and the lasso. Like OLS, ridge attempts to minimize residual sum of squares of predictors in a given model. However, ridge regression includes an additional'shrinkage' term – the square of the coefficient estimate – which shrinks the estimate of the coefficients towards zero. Two interesting implications of this design are the facts that when λ 0 the OLS coefficients are returned and when λ, coefficients will approach zero.

Machine Learning is a hot topic in the science world right now. By combining the powers and capabilities of both computers and humans, perplexing and unimaginable problems are being resolved as we speak. Machines nowadays can more easily handle the ginormous amount of data constantly being produced, and decipher the complexity of scientific discoveries. Researchers have begun to recognise the potential this science can have in a vast variety of fields, and it's finally being put into practice. On researching the topic, many of the techniques and algorithms will seem familiar to a lot of statisticians, engineers, programmers, mathematicians and quants.

It optimizes the number of the cluster when the clustering method is maximizing the variance among the clusters. If you are using for example K-means as clustering algorithm, your method will fail for every number of cluster you try to use! As you can see doesn't exist the right number of clusters, for this problem using the "naive" kmeans. BTW I've seen for kmeans and density based clustering algo, methods based on EM (expectation and maximizazion) and Bayesian information criterion (BIC) that are a little bit more robust than this method. Could you share the table of the points...just to play a little bit with them:)

Contextual bandits are a form of multi-armed bandit in which the agent has access to predictive side information (known as the context) for each arm at each time step, and have been used to model personalized news recommendation, ad placement, and other applications. In this work, we propose a multi-task learning framework for contextual bandit problems. Like multi-task learning in the batch setting, the goal is to leverage similarities in contexts for different arms so as to improve the agent's ability to predict rewards from contexts. We propose an upper confidence bound-based multi-task learning algorithm for contextual bandits, establish a corresponding regret bound, and interpret this bound to quantify the advantages of learning in the presence of high task (arm) similarity. We also describe an effective scheme for estimating task similarity from data, and demonstrate our algorithm's performance on several data sets.

The volume of data generated by internet and social networks is increasing every day, and there is a clear need for efficient ways of extracting useful information from them. As those data can take different forms, it is important to use all the available data representations for prediction. In this paper, we focus our attention on supervised classification using both regular plain, tabular, data and structural information coming from a network structure. 14 techniques are investigated and compared in this study and can be divided in three classes: the first one uses only the plain data to build a classification model, the second uses only the graph structure and the last uses both information sources. The relative performances in these three cases are investigated. Furthermore, the effect of using a graph embedding and well-known indicators in spatial statistics is also studied. Possible applications are automatic classification of web pages or other linked documents, of people in a social network or of proteins in a biological complex system, to name a few. Based on our comparison, we draw some general conclusions and advices to tackle this particular classification task: some datasets can be better explained by their graph structure (graph-driven), or by their feature set (features-driven). The most efficient methods are discussed in both cases.