The following free book is for anyone interested in machine learning.
About the book (by the author)
Any change in a system that allows you to perform better a second time on a repetition of the same task or another task drawn from the same population.
Depending on the amount and type of knowledge that the system has before the learning stage (a priori knowledge of the system), we can distinguish several situations:
- The simplest form of learning is simply to assign values to specific parameters. It is a situation where the system contains all the knowledge required for a particular type of task.
- Another primary type of learning is the storage of data as they are. This is called rote learning. An example of this type of learning is database filling.
- Knowledge acquisition process in an expert system is a type of learning process where some predefined structures (rules, frameworks, etc.) are filled with data specified directly or indirectly by an expert. In this case only the knowledge structure is known.
- The system receives a set of examples (training data) and is supposed to produce a description of this set in the context of a particular language. The primary knowledge of the system is the permitted language syntax (syntax bias) and probably some characteristics of the domain from which the examples are drawn (domain knowledge or semantic bias). This is a specific function of inductive learning and is commonly called concept learning or example learning.
- There are learning systems (for example, neural networks) that can learn to respond appropriately to data without prior knowledge. Neural networks actually use a kind of predetermined structure of knowledge to represent (a network of neuron-like elements), which, however, is very general and therefore suitable for different types of knowledge.
Like human learning, the machine learning process is influenced by the presence (or absence) of the teacher.
Content (Subject)
- introduction
- concept learning
- languages to learn
- learning version space
- Decision Trees Induction
- hedging strategies
- Search in generalization/specialization graph
- inductive logic programming
- Bayesian Approach and CDM
- useless learning
- explanation-based learning
data sheet
year: 2003
Editor: self editing
Language: English
Shape: 340 KB
License: pending review
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