Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-28T06:47:04.333Z Has data issue: false hasContentIssue false

7 - Learning

Published online by Cambridge University Press:  05 July 2014

By
David Danks
Edited by
Keith Frankish  and
William M. Ramsey
David Danks
Affiliation:
Carnegie Mellon University
Keith Frankish
Affiliation:
The Open University, Milton Keynes
William M. Ramsey
Affiliation:
University of Nevada, Las Vegas
Get access

Summary

Introduction

Learning by artificial intelligence systems – what I will typically call machine learning – has a distinguished history, and the field has experienced something of a renaissance in the past twenty years. Machine learning consists principally of a diverse set of algorithms and techniques that have been applied to problems in a wide range of domains. Any overview of the methods and applications will inevitably be incomplete, at least at the level of specific algorithms and techniques. There are many excellent introductions to the formal and statistical details of machine learning algorithms and techniques available elsewhere (e.g., Bishop 1995; Mitchell 1997; Duda, Hart, and Stork 2000; Hastie, Tibshirani, and Friedman 2001; Koller and Friedman 2009). The present chapter focuses on machine learning as a general way of “thinking about the world,” and provides a high-level characterization of the major goals of machine learning. There are a number of philosophical concerns that have been raised about machine learning, but upon closer examination, it is not always clear whether the objections really speak against machine learning specifically. Many seem rather to be directed towards machine learning as a particular instantiation of some more general phenomenon or process. One of the general morals of this chapter is that machine learning is, in many ways, less unusual or peculiar than is sometimes thought.

Keywords

neural-network learning algorithmsstructural inferencecausal learning algorithmsmachine learningBayesian networksmachine-learned model
Type
Chapter
Information
The Cambridge Handbook of Artificial Intelligence , pp. 151 - 167
Publisher: Cambridge University Press
Print publication year: 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford University Press.Google Scholar
Duda, R. O., Hart, P. E., and Stork, D. G. (2000). Pattern Classification (2nd edn.). New York: John Wiley and Sons.Google Scholar
Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning. New York: Springer.CrossRefGoogle Scholar
Mitchell, T. M. (1997). Machine Learning. New York: McGraw-Hill.Google Scholar
Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.Google Scholar
Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford University Press.Google Scholar
Cartwright, N. (1999). The Dappled World: A Study of the Boundaries of Science. Cambridge University Press.CrossRefGoogle Scholar
Cartwright, N. (2001). What is wrong with Bayes nets?, The Monist 84: 242–64.CrossRefGoogle Scholar
Cartwright, N. (2002). Against modularity, the causal Markov condition, and any link between the two: Comments on Hausman and Woodward, British Journal for the Philosophy of Science 53: 411–53.CrossRefGoogle Scholar
Chickering, D. M. (2002). Optimal structure identification with greedy search, Journal of Machine Learning Research 3: 507–54.Google Scholar
Chu, T., Glymour, C., Scheines, R., and Spirtes, P. (2003). A statistical problem for inference to regulatory structure from associations of gene expression measurements with microarrays, Bioinformatics 19: 1147–52.CrossRefGoogle ScholarPubMed
Duda, R. O., Hart, P. E., and Stork, D. G. (2000). Pattern Classification (2nd edn.). New York: John Wiley and Sons.Google Scholar
Freund, Y. (1995). Boosting a weak learning algorithm by majority, Information and Computation 121: 256–85.CrossRefGoogle Scholar
Glymour, C. and Cooper, G. F. (1999). Computation, Causation, and Discovery. Cambridge, MA: AAAI Press and MIT Press.Google Scholar
Harnad, S. (1994). Computation is just interpretable symbol manipulation; cognition isn’t, Minds and Machines 4: 379–90.CrossRefGoogle Scholar
Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning. New York: Springer.CrossRefGoogle Scholar
Hausman, D. M. and Woodward, J. (1999). Independence, invariance, and the causal Markov condition, British Journal for the Philosophy of Science 50: 521–83.CrossRefGoogle Scholar
Hausman, D. M. and Woodward, J. (2004). Manipulation and the causal Markov condition, Philosophy of Science 71: 846–56.CrossRefGoogle Scholar
Jordan, M. I. and Jacobs, R. A. (1994). Hierarchical mixtures of experts and the EM algorithm, Neural Computation 6: 181–214.CrossRefGoogle Scholar
Koller, D. and Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. Cambridge, MA: MIT Press.Google Scholar
Mitchell, T. M. (1997). Machine Learning. New York: McGraw-Hill.Google Scholar
Nisbett, R. E. and Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes, Psychological Review 84: 231–59.CrossRefGoogle Scholar
Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.Google Scholar
Ross, L. and Nisbett, R. E. (1991). The Person and the Situation: Perspectives of Social Psychology. New York: McGraw-Hill.Google Scholar
Schapire, R. E. (1990). The strength of weak learnability, Machine Learning 5: 197–227.CrossRefGoogle Scholar
Searle, J. (1980). Minds, brains, and programs, Behavioral and Brain Sciences 3: 417–24.CrossRefGoogle Scholar
Spirtes, P., Glymour, C., and Scheines, R. (1993). Causation, Prediction, and Search. Berlin: Springer. 2nd edn. (2000). Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Wolpert, D. H. (1996). The lack of a priori distinctions between learning algorithms, Neural Computation 8: 1341–90.CrossRefGoogle Scholar
Wolpert, D. H. and Macready, W. G. (1997). No Free Lunch theorems for optimization, IEEE Transactions on Evolutionary Computation 1: 67–82.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

  • Learning
  • Edited by Keith Frankish, The Open University, Milton Keynes, William M. Ramsey, University of Nevada, Las Vegas
  • Book: The Cambridge Handbook of Artificial Intelligence
  • Online publication: 05 July 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139046855.011
Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

  • Learning
  • Edited by Keith Frankish, The Open University, Milton Keynes, William M. Ramsey, University of Nevada, Las Vegas
  • Book: The Cambridge Handbook of Artificial Intelligence
  • Online publication: 05 July 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139046855.011
Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Learning
  • Edited by Keith Frankish, The Open University, Milton Keynes, William M. Ramsey, University of Nevada, Las Vegas
  • Book: The Cambridge Handbook of Artificial Intelligence
  • Online publication: 05 July 2014
  • Chapter DOI: https://doi.org/10.1017/CBO9781139046855.011
Available formats
×