Feature extraction has gained increasing attention in the field of machine learning, as in order to detect patterns, extract information, or predict future observations from big data, the urge of informative features is crucial. The process of extracting features is highly linked to dimensionality reduction as it implies the transformation of the data from a sparse high-dimensional space, to higher level meaningful abstractions. This dissertation employs Neural Networks for distributed paragraph representations, and Latent Dirichlet Allocation to capture higher level features of paragraph vectors. Although Neural Networks for distributed paragraph representations are considered the state of the art for extracting paragraph vectors, we show that a quick topic analysis model such as Latent Dirichlet Allocation can provide meaningful features too. We evaluate the two methods on the CMU Movie Summary Corpus, a collection of 25,203 movie plot summaries extracted from Wikipedia. Finally, for both approaches, we use K-Nearest Neighbors to discover similar movies, and plot the projected representations using T-Distributed Stochastic Neighbor Embedding to depict the context similarities. These similarities, expressed as movie distances, can be used for movies recommendation. The recommended movies of this approach are compared with the recommended movies from IMDB, which use a collaborative filtering recommendation approach, to show that our two models could constitute either an alternative or a supplementary recommendation approach.

Many articles have been written about the top machine learning algorithms: click here and here for instance. Most of them seem to define top as oldest, and thus most used, ignoring modern, efficient algorithms fit for big data, such as indexation, attribution modeling, collaborative filtering, or recommendation engines used by companies such as Amazon, Google, or Facebook. I received this morning and advertisement for a (self-published) book called Master Machine Learning Algorithms, and I could not resist to post the author's list of top 10 machine learning algorithms:: Some of these techniques such as Naive Bayes (variables are almost never uncorrelated), Linear Discriminant Analysis (clusters are almost never separated by hyperplanes), or Linear Regression (numerous model assumptions - including linearity - are almost always violated in real data) have been so abused that I would hesitate teaching them. This is not a criticism of the book; most textbooks mention pretty much the same algorithms, and in this case, even skipping all graph-related algorithms. Even k Nearest Neighbors have modern, fast implementations not covered in traditional books - we are indeed working on this topic and expect to have an article published shortly about it.

With the development and growth of ecommerce platforms like Shopify, the number of small- and medium- sized ecommerce businesses is growing at an impressive rate. But, with this growth comes a growth in market opportunities for the online villains and fraudsters out there who are looking to make a quick buck. It used to be that only huge corporations had the resources they needed to detect fraud and protect themselves from its damages. But, in this era of big data and data science for all, even small mom and pop ecommerce shops have access to the tools they need to protect themselves from evil fraudsters. This article introduces some common sources of fraud problems in ecommerce, and how you can use data science technologies or techniques to protect your business (or soon-to-be business) from risk.

Guest blog post by Dalila Benachenhou, originally posted here. Dalila is Professor at George Washington University. In this article, benchmarks were computed on a specific data set, for Geico Calls Prediction, comparing Random Forests, Neural Networks, SVM, FDA, K Nearest Neighbors, C5.0 (Decision Trees), Logistic Regression, and Cart. Introduction: In the first blog, we decided on the predictors. We knew that different predictive models have different assumptions about their predictors.

Supervised learning with large scale labeled datasets and deep layered models has made a paradigm shift in diverse areas in learning and recognition. However, this approach still suffers generalization issues under the presence of a domain shift between the training and the test data distribution. In this regard, unsupervised domain adaptation algorithms have been proposed to directly address the domain shift problem. In this paper, we approach the problem from a transductive perspective. We incorporate the domain shift and the transductive target inference into our framework by jointly solving for an asymmetric similarity metric and the optimal transductive target label assignment. We also show that our model can easily be extended for deep feature learning in order to learn features which are discriminative in the target domain. Our experiments show that the proposed method significantly outperforms state-of-the-art algorithms in both object recognition and digit classification experiments by a large margin.

This article was written by Natasha Latysheva. Here we publish a short version, with references to full source code in the original article. In machine learning, you may often wish to build predictors that allows to classify things into categories based on some set of associated values. For example, it is possible to provide a diagnosis to a patient based on data from previous patients. Many algorithms have been developed for automated classification, and common ones include random forests, support vector machines, Naïve Bayes classifiers, and many types of neural networks.

This method just brings together the previous functions and should be relatively self-explanatory. One potentially confusing point may be the very end of the script – instead of just calling main() to run the script, it is useful to instead first check if name "main" . This would make no difference at all if you only want to run this script as is from the command line or in an interactive shell – when reading the source code, the Python interpreter would set the special name variable to "main" and run everything. However, say that you wanted to just import the functions to another module (another .py

Spam is a kind of messaging where the cost of sending is usually negligible and the receiver and the ISP pays the cost in terms of bandwidth usage. An example of a manual approach to detecting spam is using knowledge engineering. If the subject line of an email contains words'Buy viagra' its spam These rules can be configured by the user himself or by the email provider and if correctly thought out and executed this technique can be effectively be used to combat spam. This is a blog post about one such implementation. However, a manual rules based approach doesn't scale because of active human spammers circumventing any manual rules.

A key question in modern statistics is how to make fast and reliable inferences for complex, high-dimensional data. While there has been much interest in sparse techniques, current methods do not generalize well to data with nonlinear structure. In this work, we present an orthogonal series estimator for predictors that are complex aggregate objects, such as natural images, galaxy spectra, trajectories, and movies. Our series approach ties together ideas from kernel machine learning, and Fourier methods. We expand the unknown regression on the data in terms of the eigenfunctions of a kernel-based operator, and we take advantage of orthogonality of the basis with respect to the underlying data distribution, P, to speed up computations and tuning of parameters. If the kernel is appropriately chosen, then the eigenfunctions adapt to the intrinsic geometry and dimension of the data. We provide theoretical guarantees for a radial kernel with varying bandwidth, and we relate smoothness of the regression function with respect to P to sparsity in the eigenbasis. Finally, using simulated and real-world data, we systematically compare the performance of the spectral series approach with classical kernel smoothing, k-nearest neighbors regression, kernel ridge regression, and state-of-the-art manifold and local regression methods.

The $k$-nearest neighbour ($k$-NN) classifier is one of the oldest and most important supervised learning algorithms for classifying datasets. Traditionally the Euclidean norm is used as the distance for the $k$-NN classifier. In this thesis we investigate the use of alternative distances for the $k$-NN classifier. We start by introducing some background notions in statistical machine learning. We define the $k$-NN classifier and discuss Stone's theorem and the proof that $k$-NN is universally consistent on the normed space $R^d$. We then prove that $k$-NN is universally consistent if we take a sequence of random norms (that are independent of the sample and the query) from a family of norms that satisfies a particular boundedness condition. We extend this result by replacing norms with distances based on uniformly locally Lipschitz functions that satisfy certain conditions. We discuss the limitations of Stone's lemma and Stone's theorem, particularly with respect to quasinorms and adaptively choosing a distance for $k$-NN based on the labelled sample. We show the universal consistency of a two stage $k$-NN type classifier where we select the distance adaptively based on a split labelled sample and the query. We conclude by giving some examples of improvements of the accuracy of classifying various datasets using the above techniques.