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

An important problem in database systems is answering queries quickly. This paper surveys a number of algorithms for efficiently answering range queries. First a set of “loGical structures” is described and ‘then their implementation in primary and secondary memories is discussed. The algorithms included are of both “practical” and “theoretical” interest. Although some new results are presented, the primary purpose of this paper is to collect together the known results on range searching and to present them in a common terminology. (Submitted to ACM Transactions on Database Systems) -Work partially supported by Department of Energy

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A survey of algorithms and data structures for range searching

Publisher Summary This chapter discusses M and N plots and describes a method for scatterplotting four-dimensional data. Conventional scatterplots use dots on a rectangular coordinate system to graphically represent two-dimensional vectors. An M and N plot represents (M+N)-dimensional vectors by plotting M coordinates in one coordinate system and N coordinates in a second coordinate system. The two dots representing a point are connected by a straight line segment. Thus, scatterplots are 2 and 0 plots. A 1 and 0 plot—the dot plot—is sometimes used to plot one-dimensional data, plotting each point as a dot. There are two ways to draw M and N plots of two-dimensional data: the conventional scatterplot (2 and 0 plot) and the 1and 1 plot. A 1 and 1 plot represents a two-dimensional point (x,y) by 2 dots and a line segment on a pair of parallel coordinate axis. The basic ingredients of a 2 and 2 plot are a pair of coordinate axes and a segment. With a PRIM 9-1ike graphical device, it is possible to draw 3 and 3 plots of six-dimensional data.

M and n plots

We propose a generalization of the lasso that allows the model coefficients to vary as a function of a general set of some prespecified modifying variables. These modifiers might be variables such ...

A Pliable Lasso

For most of this book, the fitting (learning) of models has been achieved by minimizing a sum of squares for regression, or by minimizing cross-entropy for classification. In fact, both of these minimizations are instances of the maximum likelihood approach to fitting.

Model Inference and Averaging

A network based on splines is described. It automatically adapts the number of units, unit parameters, and the architecture of the network for each application.

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Jerome H. Friedman

Papers

A survey of algorithms and data structures for range searching

M and n plots

A Pliable Lasso

Model Inference and Averaging

Adaptive Spline Networks