To install click the Add extension button. That's it.

The source code for the WIKI 2 extension is being checked by specialists of the Mozilla Foundation, Google, and Apple. You could also do it yourself at any point in time.

Kelly Slayton
Congratulations on this excellent venture… what a great idea!
Alexander Grigorievskiy
I use WIKI 2 every day and almost forgot how the original Wikipedia looks like.
Live Statistics
English Articles
Improved in 24 Hours
Added in 24 Hours
Show all languages
What we do. Every page goes through several hundred of perfecting techniques; in live mode. Quite the same Wikipedia. Just better.

Instance-based learning

From Wikipedia, the free encyclopedia

In machine learning, instance-based learning (sometimes called memory-based learning[1]) is a family of learning algorithms that, instead of performing explicit generalization, compare new problem instances with instances seen in training, which have been stored in memory. Because computation is postponed until a new instance is observed, these algorithms are sometimes referred to as "lazy."[2]

It is called instance-based because it constructs hypotheses directly from the training instances themselves.[3] This means that the hypothesis complexity can grow with the data:[3] in the worst case, a hypothesis is a list of n training items and the computational complexity of classifying a single new instance is O(n). One advantage that instance-based learning has over other methods of machine learning is its ability to adapt its model to previously unseen data. Instance-based learners may simply store a new instance or throw an old instance away.

Examples of instance-based learning algorithms are the k-nearest neighbors algorithm, kernel machines and RBF networks.[2]: ch. 8  These store (a subset of) their training set; when predicting a value/class for a new instance, they compute distances or similarities between this instance and the training instances to make a decision.

To battle the memory complexity of storing all training instances, as well as the risk of overfitting to noise in the training set, instance reduction algorithms have been proposed.[4]

YouTube Encyclopedic

  • 1/3
    3 618
    4 293
    2 602
  • Instance Based Learning Now - Georgia Tech - Machine Learning
  • Machine Learning #10 - Instance based Learning #1 - k-Nearest Neighbours
  • Machine Learning #13 - Instance based Learning #4 - Das Konzept "Case Based Reasoning"


See also


  1. ^ Walter Daelemans; Antal van den Bosch (2005). Memory-Based Language Processing. Cambridge University Press.
  2. ^ a b Tom Mitchell (1997). Machine Learning. McGraw-Hill.
  3. ^ a b Stuart Russell and Peter Norvig (2003). Artificial Intelligence: A Modern Approach, second edition, p. 733. Prentice Hall. ISBN 0-13-080302-2
  4. ^ D. Randall Wilson; Tony R. Martinez (2000). "Reduction techniques for instance-based learning algorithms". Machine Learning.
This page was last edited on 24 May 2021, at 15:45
Basis of this page is in Wikipedia. Text is available under the CC BY-SA 3.0 Unported License. Non-text media are available under their specified licenses. Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc. WIKI 2 is an independent company and has no affiliation with Wikimedia Foundation.