Pattern Recognition and Machine Learning

doi:10.1198/TECH.2007.S518

Journal Article•DOI•

Pattern Recognition and Machine Learning

01 Aug 2007-Technometrics (Taylor & Francis)-Vol. 49, Iss: 3, pp 366-366

TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.

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Abstract: (2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.

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Journal Article•DOI•

SignalP 5.0 improves signal peptide predictions using deep neural networks

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Jose Juan Almagro Armenteros¹, Konstantinos D. Tsirigos, Casper Kaae Sønderby², Thomas Nordahl Petersen¹, Ole Winther², Ole Winther¹, Søren Brunak², Søren Brunak¹, Gunnar von Heijne³, Gunnar von Heijne⁴, Henrik Nielsen¹ - Show less +7 more•Institutions (4)

Technical University of Denmark¹, University of Copenhagen², Science for Life Laboratory³, Stockholm University⁴

18 Feb 2019-Nature Biotechnology

TL;DR: A deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs is presented.

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Abstract: Signal peptides (SPs) are short amino acid sequences in the amino terminus of many newly synthesized proteins that target proteins into, or across, membranes. Bioinformatic tools can predict SPs from amino acid sequences, but most cannot distinguish between various types of signal peptides. We present a deep neural network-based approach that improves SP prediction across all domains of life and distinguishes between three types of prokaryotic SPs.

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2,732 citations

Book•

Learning to Rank for Information Retrieval

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Tie-Yan Liu¹•Institutions (1)

Microsoft¹

27 Jun 2009

TL;DR: Three major approaches to learning to rank are introduced, i.e., the pointwise, pairwise, and listwise approaches, the relationship between the loss functions used in these approaches and the widely-used IR evaluation measures are analyzed, and the performance of these approaches on the LETOR benchmark datasets is evaluated.

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Abstract: This tutorial is concerned with a comprehensive introduction to the research area of learning to rank for information retrieval. In the first part of the tutorial, we will introduce three major approaches to learning to rank, i.e., the pointwise, pairwise, and listwise approaches, analyze the relationship between the loss functions used in these approaches and the widely-used IR evaluation measures, evaluate the performance of these approaches on the LETOR benchmark datasets, and demonstrate how to use these approaches to solve real ranking applications. In the second part of the tutorial, we will discuss some advanced topics regarding learning to rank, such as relational ranking, diverse ranking, semi-supervised ranking, transfer ranking, query-dependent ranking, and training data preprocessing. In the third part, we will briefly mention the recent advances on statistical learning theory for ranking, which explain the generalization ability and statistical consistency of different ranking methods. In the last part, we will conclude the tutorial and show several future research directions.

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2,515 citations

Journal Article•DOI•

A survey of deep neural network architectures and their applications

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Weibo Liu¹, Zidong Wang¹, Xiaohui Liu¹, Nianyin Zeng², Yurong Liu³, Yurong Liu⁴, Fuad E. Alsaadi³ - Show less +3 more•Institutions (4)

Brunel University London¹, Xiamen University², King Abdulaziz University³, Yangzhou University⁴

19 Apr 2017-Neurocomputing

TL;DR: This work was supported in part by the Royal Society of the UK, the National Natural Science Foundation of China, and the Alexander von Humboldt Foundation of Germany.

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2,404 citations

Cites background from "Pattern Recognition and Machine Lea..."

...Pattern recognition is a scientific discipline which aims to identify the pattern of a given input value [14]....
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Journal Article•DOI•

Stochastic variational inference

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Matthew D. Hoffman¹, David M. Blei², Chong Wang³, John Paisley⁴•Institutions (4)

Adobe Systems¹, Princeton University², Carnegie Mellon University³, University of California, Berkeley⁴

01 Jan 2013-Journal of Machine Learning Research

TL;DR: Stochastic variational inference lets us apply complex Bayesian models to massive data sets, and it is shown that the Bayesian nonparametric topic model outperforms its parametric counterpart.

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Abstract: We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Using stochastic variational inference, we analyze several large collections of documents: 300K articles from Nature, 1.8M articles from The New York Times, and 3.8M articles from Wikipedia. Stochastic inference can easily handle data sets of this size and outperforms traditional variational inference, which can only handle a smaller subset. (We also show that the Bayesian nonparametric topic model outperforms its parametric counterpart.) Stochastic variational inference lets us apply complex Bayesian models to massive data sets.

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2,291 citations

Cites background or methods from "Pattern Recognition and Machine Lea..."

...70 51 v3 [...
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...Statistical machine learning research has addressed some of these challenges by developing the field of probabilistic modeling, a field that provides an elegant approach to developing new methods for analyzing data (Pearl, 1988; Jordan, 1999; Bishop, 2006; Koller and Friedman, 2009; Murphy, 2012)....
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...Further, the optimal mean-field distribution, without regard to its particular functional form, has factors in these families (Bishop, 2006)....
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...Form intermediate global variational parameters, as thoug we were running classical coordinate ascent and the sampled data point were repeatedN imes to form the collection....
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...We have defined the objective function in Equation 8 and the variational family in Equations 9, 10 and 11....
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Book•

Neural Networks and Deep Learning

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Charu C. Aggarwal

01 Jan 2018

2,291 citations

Cites background or methods from "Pattern Recognition and Machine Lea..."

...Even though the Fisher discriminant uses an objective function that appears o to be different from least-squares regression, it turns out to be a special case of least-squares regression in which the binary response variable is used as the regressand [40]....
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...Several proofs of this result are available in the literature [3, 6, 40, 41]....
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...The reader is recommended to refer to [2, 3, 40, 177] for basic knowledge on machine learning methods....
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Pattern Recognition and Machine Learning

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Cites background from "Pattern Recognition and Machine Lea..."

Cites background or methods from "Pattern Recognition and Machine Lea..."

Cites background or methods from "Pattern Recognition and Machine Lea..."

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