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About the Book and Supporting Material

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/9780691151687.003.0001
Subject:
Physics, Particle Physics / Astrophysics / Cosmology

This chapter begins by discussing the meaning of data mining, machine learning, and knowledge discovery. Data mining, machine learning, and knowledge discovery refer to research areas which can all ... 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


Artificial Intelligence and the Ethics of Self-Learning Robots

Shannon Vallor and George A. Bekey

in Robot Ethics 2.0: From Autonomous Cars to Artificial Intelligence

Published in print:
2017
Published Online:
October 2017
ISBN:
9780190652951
eISBN:
9780190652982
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780190652951.003.0022
Subject:
Philosophy, Philosophy of Science

The convergence of robotics technology with the science of artificial intelligence is rapidly enabling the development of robots that emulate a wide range of intelligent human behaviors. Recent ... More


Author Comments

Shimodaira Hidetoshi, Sugiyama Masashi, Storkey Amos, Gretton Arthur, and David Shai-Ben

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/9780262170055.003.0011
Subject:
Computer Science, Machine Learning

This chapter presents comments by the authors about dataset shift in machine learning. Topics covered include covariate shift and misspecification; and whether importance weighting is needed under ... More


Automatic Facial Expression Recognition

Jacob Whitehill, Marian Stewart Bartlett, and Javier R. Movellan

in Social Emotions in Nature and Artifact

Published in print:
2013
Published Online:
January 2014
ISBN:
9780195387643
eISBN:
9780199369195
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780195387643.003.0007
Subject:
Psychology, Cognitive Models and Architectures, Cognitive Psychology

In this chapter we define the problem space and describe the core components of automatic facial expression recognition systems. In particular, we discuss the most prominent methods of face ... More


The Blessing and the Curse of the Multiplicative Updates

Daniel Friedman and Barry Sinervo

in Evolutionary Games in Natural, Social, and Virtual Worlds

Published in print:
2016
Published Online:
August 2016
ISBN:
9780199981151
eISBN:
9780190466657
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199981151.003.0010
Subject:
Economics and Finance, Behavioural Economics

This chapter shows how replicator dynamics (in a setting with no frequency dependence) correspond to multiplicative updates studied by computer scientists in the context of online learning. The ... More


Building Learning Systems

John E. Kelly and Steve Hamm

in Smart Machines: IBM's Watson and the Era of Cognitive Computing

Published in print:
2013
Published Online:
November 2015
ISBN:
9780231168564
eISBN:
9780231537278
Item type:
chapter
Publisher:
Columbia University Press
DOI:
10.7312/columbia/9780231168564.003.0002
Subject:
Business and Management, Information Technology

This chapter examines machine learning systems. The development of IBM's Watson represented a major advance in the science of machine learning, a branch of artificial intelligence that focuses on ... More


Categorization as nonparametric Bayesian density estimation

Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini, and Daniel J. Navarro

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/9780199216093.003.0014
Subject:
Psychology, Cognitive Psychology

The authors apply the state of the art techniques from machine learning and statistics to reconceptualize the problem of unsupervised category learning, and to relate it to previous psychologically ... More


Coda: The Algorithmic Imagination

Ed Finn

in What Algorithms Want: Imagination in the Age of Computing

Published in print:
2017
Published Online:
September 2017
ISBN:
9780262035927
eISBN:
9780262338837
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262035927.003.0007
Subject:
Computer Science, Programming

The coda retraces the genealogy of the algorithm to consider our future prospects for achieving the twinned desires embedded in the heart of effective computability: the quest for universal knowledge ... More


Combination of Statistical Word Alignments Based on Multiple Preprocessing Schemes

Jakob Elming, Nizar Habash, and Josep M. Crego

in Learning Machine Translation

Published in print:
2008
Published Online:
August 2013
ISBN:
9780262072977
eISBN:
9780262255097
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262072977.003.0005
Subject:
Computer Science, Machine Learning

This chapter presents an approach to using multiple preprocessing (tokenization) schemes to improve statistical word alignments. In this approach, the text to align is tokenized before statistical ... More


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