Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

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Scikit-learn: Machine Learning in Python

In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene. The features are invariant to image scale and rotation, and are shown to provide robust matching across a substantial range of affine distortion, change in 3D viewpoint, addition of noise, and change in illumination. The features are highly distinctive, in the sense that a single feature can be correctly matched with high probability against a large database of features from many images. This paper also describes an approach to using these features for object recognition. The recognition proceeds by matching individual features to a database of features from known objects using a fast nearest-neighbor algorithm, followed by a Hough transform to identify clusters belonging to a single object, and finally performing verification through least-squares solution for consistent pose parameters. This approach to recognition can robustly identify objects among clutter and occlusion while achieving near real-time performance.

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Distinctive Image Features from Scale-Invariant Keypoints

LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.

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LIBSVM: A library for support vector machines

SUMMARY We propose a new method for estimation in linear models. The 'lasso' minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described.

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Regression Shrinkage and Selection via the Lasso

This volume provides a unified approach to the study of predictive learning, i.e., generalization from examples. It contains an up-to-date review and in-depth treatment of major issues and methods related to predictive learning in statistics, Artificial Neural Networks (ANN), and pattern recognition. Topics range from theoretical modeling and adaptive computational methods to empirical comparisons between statistical and ANN methods, and applications. Most contributions fall into one of the three themes: unified framework for the study of predictive learning in statistics and ANNs; similarities and differences between statistical and ANN methods for nonparametric estimation (learning); and fundamental connections between artificial and biological learning systems.

From Statistics to Neural Networks: Theory and Pattern Recognition Applications

Lasso and Elastic-Net Regularized Generalized Linear Models [R package glmnet version 4.1-1]

We consider the problem of summarizing a scatterplot with a smooth, monotone curve. A solution that combines local averaging and isotonic regression is proposed, and we demonstrate its use with two examples. In the second example, the procedure is applied, in a regression setting, to some data from Box and Cox (1964), and it is shown how this new procedure generalizes Box and Cox's well-known family of transformations. In the same example, the bootstrap is applied to obtain a measure of the variability of the procedure.

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The Monotone Smoothing of Scatterplots

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PRIM-9: An Interactive Multi-dimensional Data Display and Analysis System.

We propose several methods for estimating edge-sparse and nodesparse graphical models based on lasso and grouped lasso penalties. We develop ecien t algorithms for tting these models when the numbers of nodes and potential edges are large. We compare them to competing methods including the graphical lasso and SPACE (Peng, Wang, Zhou & Zhu 2008). Surprisingly, we nd that for edge selection, a simple method based on univariate screening of the elements of the empirical correlation matrix usually performs as well or better than all of the more complex methods proposed here and elsewhere. Running title: Applications of the lasso and grouped lasso

Jerome H. Friedman

Papers

From Statistics to Neural Networks: Theory and Pattern Recognition Applications

Lasso and Elastic-Net Regularized Generalized Linear Models [R package glmnet version 4.1-1]

The Monotone Smoothing of Scatterplots

PRIM-9: An Interactive Multi-dimensional Data Display and Analysis System.

Applications of the lasso and grouped lasso to the estimation of sparse graphical models