Learning Task

A Learning Task is an Inductive Reasoning Task that requires a Conclusion based on a Set of Learning Records.

• AKA: Machine Learning Task, Learning Scheme, Learning Style, Automatic Learning Task.
• Context:
  • Input:
    • One or more Training Record Set.
    • Optionally, one or more Target Features.
    • Optionally, a Optimization Function (e.g. Cost-Benefit Function)
    • Optionally, a Testing Record Set to test Performance.
    • Optionally, a Decision on whether a Predictive Model is required.
  • Output:
    • Improved performance on future tasks.
    • Optionally, a Prediction for each Testing Record.
    • Optionally, a Model (such as a Cluster Model or a Predictive Model).
    • Optionally, an Inductive Argument.
  • Performance:
    • Its Performance can be measured in terms of improved performance over time with experience.
    • Its Performance can depend on the Data Noise.
  • It can be:
    • a Supervised Learning Task (Fully-Supervised or Semi-Supervised.
    • an Unsupervised Learning Task.
  • It can be:
    • a Model-based Learning Task.
    • a Lazy Learning Task.
  • It can be a Predictive Function Learning Task.
    • a Classification Function Learning Task.
    • a Estimation Function Learning Task.
  • It can be a Reinforcement Learning Task.
  • Its Predictive Model can be used for Deductive Reasoning.
  • It can range from:
    • being a Simple Input Learning Task.
    • to being a Complex Input Learning Task.
  • It can range from:
    • being a Simple Output Learning Task.
    • to being a Complex Output Learning Task.
  • It can be an Active Learning Task, if there can be interaction.
  • It can be:
    • a Machine Learning Task.
    • a Statistical Learning Task.
    • a Data Mining Task.
• Example(s):
  • Predict whether a person is ill or not, and what remedy to apply.
  • Acquire a Language be hearing and talking.
  • Model where a thrown object will land under a gravitational force.
• See: Machine Learning Research, Statistical Modeling Task

References

2009

• (Wikipedia, 2009) http://en.wikipedia.org/wiki/Machine_learning
  • As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to "learn". At a general level, there are two types of learning: inductive, and deductive. ...
• http://dli.grainger.uiuc.edu/glossary.htm
  • The ability of a machine to improve its performance based on previous results.
• http://clopinet.com/isabelle/Projects/ETH/Exam_Questions.html
  • Machine Learning is a discipline dedicated to the design and study of artificial learning systems, particularly systems that learn from examples. Learning machines include linear models, artificial neural networks, and decisions trees.
• Jerome H. Friedman http://www-stat.stanford.edu/brochure/part4.html#friedman
  • One such area that is receiving considerable recent attention is machine learning ("neural networks"). Here one has a system under study that responds to a set of simultaneous input signals. The response is characterized by a set of output signals. The goal is to learn the relationship between the inputs and the outputs in the most general way possible. This exercise generally has two purposes: prediction and understanding. With prediction one is given a set of input values and wishes to predict or forecast likely values of the corresponding outputs without having to actually run the system. Sometimes prediction is the only purpose. Often, however, one wishes to use the derived relationship to gain understanding of how the system works. Such knowledge is often useful in its own right, for example in science, or it may be used to help improve the characteristics of the system, as in industrial or engineering applications.

2008

• (Wilson, 2008a) => Bill Wilson. (2008). "The Machine Learning Dictionary for COMP9414. University of New South Wales, Australia.
  • machine learning: Machine learning is said to occur in a program that can modify some aspect of itself, often referred to as its state, so that on a subsequent execution with the same input, a different (hopefully better) output is produced. See unsupervised learning and supervised learning, and also function approximation algorithms and symbolic learning algorithms.

2000

• (Witten & Frank, 2000) => Ian H. Witten, and Eibe Frank. (2000). "Data Mining: practical machine learning tools and techniques with Java implementations. Morgan Kaufmann.
  • Four basically different styles of learning appear in data mining applications. In classification learning, a learning scheme takes a set of classified examples from which it is expected to learn a way of classifying unseen examples. In association learning, any association between features is sought, not just ones that predict a particular class value. In clustering, groups of examples that belong together are sought. In numeric prediction, the outcome to be predicted is not a discrete class but a numeric quantity. Regardless of the type of learning involve, we call then thing to be learn the concept, and the output produce by a learning scheme the concept description.