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Fast Computation on Massive Data Sets

Željko Ivezi, Andrew J. Connolly, Jacob T. VanderPlas, Alexander Gray, Željko Ivezi, Andrew J. Connolly, Jacob T. VanderPlas, and Alexander Gray

in Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data

Published in print:
2014
Published Online:
October 2017
ISBN:
9780691151687
eISBN:
9781400848911
Item type:
chapter
Publisher:
Princeton University Press
DOI:
10.23943/princeton/9780691151687.003.0002
Subject:
Physics, Particle Physics / Astrophysics / Cosmology

This chapter describes basic concepts and tools for tractably performing the computations described in the rest of this book. The need for fast algorithms for such analysis subroutines is becoming ... More


Heterogeneous Gain Learning and Long Swings in Asset Prices

Blake LeBaron

in Rethinking Expectations: The Way Forward for Macroeconomics

Published in print:
2013
Published Online:
October 2017
ISBN:
9780691155234
eISBN:
9781400846450
Item type:
chapter
Publisher:
Princeton University Press
DOI:
10.23943/princeton/9780691155234.003.0006
Subject:
Economics and Finance, Macro- and Monetary Economics

This chapter focuses on heterogeneous gain learning and long swings in asset prices. Many asset prices deviate from their fundamental values, yielding potential long-run predictability. Asset price ... More


Modelling Productivity with the Gradual Learning Algorithm: The Problem of Accidentally Exceptionless Generalizations

ADAM ALBRIGHT and BRUCE HAYES

in Gradience in Grammar: Generative Perspectives

Published in print:
2006
Published Online:
January 2010
ISBN:
9780199274796
eISBN:
9780191705861
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199274796.003.0010
Subject:
Linguistics, Syntax and Morphology

This chapter develops a model that can handle all configurations of gradience and categoricalness. It believes that the solution lies in the trade-off between reliability and generality. It shows how ... More


The Effects of Order: A Constraint-Based Explanation

Stellan Ohlsson

in In Order to Learn: How the sequence of topics influences learning

Published in print:
2007
Published Online:
April 2010
ISBN:
9780195178845
eISBN:
9780199893751
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780195178845.003.0011
Subject:
Psychology, Cognitive Psychology

This chapter presents a computational model that shows how information migrates from declarative to procedural knowledge and provides a powerful new learning mechanism for machine-learning ... More


Distributional learning of syntax

Nick Chater, Alexander Clark, John Goldsmith, and Amy Perfors

in Empiricism and Language Learnability

Published in print:
2015
Published Online:
August 2015
ISBN:
9780198734260
eISBN:
9780191801891
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198734260.003.0004
Subject:
Psychology, Cognitive Psychology, Developmental Psychology

This chapter takes a theoretical and computational perspective on the learning of syntax, arguing that techniques of distributional learning, as studied by the American structuralists, can form the ... More


The Geometric Basis of Semi-Supervised Learning

Sindhwani Vikas, Belkin Misha, and Niyogi Partha

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0012
Subject:
Computer Science, Machine Learning

This chapter presents an algorithmic framework for semi-supervised inference based on geometric properties of probability distributions. This approach brings together Laplacian-based spectral ... More


Analysis of Benchmarks

Chapelle Olivier, Schölkopf Bernhard, and Zien Alexander

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0021
Subject:
Computer Science, Machine Learning

This chapter assesses the strengths and weaknesses of different semi-supervised learning (SSL) algorithms through inviting the authors of each chapter in this book to apply their algorithms to eight ... More


Spectral Methods for Dimensionality Reduction

Saul Lawrence K., Weinberger Kilian Q., Sha Fei, Ham Jihun, and Lee Daniel D.

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0016
Subject:
Computer Science, Machine Learning

This chapter provides an overview of unsupervised learning algorithms that can be viewed as spectral methods for linear and nonlinear dimensionality reduction. Spectral methods have recently emerged ... More


An Adversarial View of Covariate Shift and a Minimax Approach

Globerson Amir, Hui Teo Choon, Smola Alex, and Roweis Sam

in Dataset Shift in Machine Learning

Published in print:
2008
Published Online:
August 2013
ISBN:
9780262170055
eISBN:
9780262255103
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262170055.003.0010
Subject:
Computer Science, Machine Learning

This chapter considers an adversarial model where the learning algorithm attempts to construct a predictor that is robust to deletion of features at test time. The problem is formulated as finding ... More


Machine Learning

James H. Faghmous

in Systems Science and Population Health

Published in print:
2017
Published Online:
March 2017
ISBN:
9780190492397
eISBN:
9780190492427
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780190492397.003.0011
Subject:
Public Health and Epidemiology, Epidemiology, Public Health

This chapter introduces non-computational scientists to the general field of machine learning and its methods. The chapter begins by outlining the common structure of machine learning applications, ... More


Data-Dependent Regularization

Corduneanu Adrian and Jaakkola Tommi

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0010
Subject:
Computer Science, Machine Learning

This chapter considers two ways of representing the topology over examples, either based on complete knowledge of the marginal density or by grouping together examples whose labels should be related. ... More


Geometry of Covariate Shift with Applications to Active Learning

Kanamori Takafumi and Shimodaira Hidetoshi

in Dataset Shift in Machine Learning

Published in print:
2008
Published Online:
August 2013
ISBN:
9780262170055
eISBN:
9780262255103
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262170055.003.0006
Subject:
Computer Science, Machine Learning

This chapter examines learning algorithms under the covariate shift in which training and test data are drawn from different distributions. Using a naive estimator under the covariate shift, such as ... More


Binary Classification under Sample Selection Bias

Hein Matthias

in Dataset Shift in Machine Learning

Published in print:
2008
Published Online:
August 2013
ISBN:
9780262170055
eISBN:
9780262255103
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262170055.003.0003
Subject:
Computer Science, Machine Learning

This chapter examines the problem of binary classification under sample selection bias from a decision-theoretic perspective. Starting from a derivation of the necessary and sufficient conditions for ... More


Learning and the complexity of Ø‐marking

Sebastian Bank and Jochen Trommer

in Understanding and Measuring Morphological Complexity

Published in print:
2015
Published Online:
May 2015
ISBN:
9780198723769
eISBN:
9780191791109
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198723769.003.0010
Subject:
Linguistics, Syntax and Morphology, Theoretical Linguistics

Zero marking of inflectional categories (zero exponence) is often regarded as a major source of complexity in morphological systems (cf. e.g. Anderson 1992, Wunderlich and Fabri 1994, Segel 2008). ... More


The value of rational analysis:: An assessment of causal reasoning and learning

Steven Sloman and Philip M. Fernbach

in The Probabilistic Mind:: Prospects for Bayesian cognitive science

Published in print:
2008
Published Online:
March 2012
ISBN:
9780199216093
eISBN:
9780191695971
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199216093.003.0021
Subject:
Psychology, Cognitive Psychology

This chapter provides a skeptical analysis of the technical machinery of causal Bayesian networks. It points out that this machinery provides valuable insights into how the representational power of ... More


Algorithms, Agents, and Ontologies

Paul Kockelman

in The Art of Interpretation in the Age of Computation

Published in print:
2017
Published Online:
July 2017
ISBN:
9780190636531
eISBN:
9780190636562
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780190636531.003.0007
Subject:
Linguistics, Sociolinguistics / Anthropological Linguistics

This chapter details the inner workings of spam filters, algorithmic devices that separate desirable messages from undesirable messages. It argues that such filters are a particularly important kind ... More


Stimulating Innovation through Big Data

Russell Walker

in From Big Data to Big Profits: Success with Data and Analytics

Published in print:
2015
Published Online:
August 2015
ISBN:
9780199378326
eISBN:
9780199378340
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199378326.003.0010
Subject:
Economics and Finance, Financial Economics

This chapter examines the role of Big Data in enabling innovation in firms. The creation of new data at a large scale, as well as the integration of sensor data from the Internet of Things, provides ... More


Frequency Models

James B. Elsner and Thomas H. Jagger

in Hurricane Climatology: A Modern Statistical Guide Using R

Published in print:
2013
Published Online:
November 2020
ISBN:
9780199827633
eISBN:
9780197563199
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780199827633.003.0011
Subject:
Earth Sciences and Geography, Meteorology and Climatology

Here in Part II, we focus on statistical models for understanding and predicting hurricane climate. This chapter shows you how to model hurricane ... More


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