Machine Learning and Data Mining
Igor Kononenko
Language: English
Pages: 480
ISBN: 1904275214
Format: PDF / Kindle (mobi) / ePub
Data mining is often referred to by real-time users and software solutions providers as knowledge discovery in databases (KDD). Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. This book has been written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining.
Suitable for advanced undergraduates and their tutors at postgraduate level in a wide area of computer science and technology topics as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to the libraries and bookshelves of the many companies who are using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions.
- Provides an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining
- A valuable addition to the libraries and bookshelves of companies using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions
An Introduction to Quantum Computing
Given). Because of the absence of an explicitly defined dependent variable, association learning is often considered unsupervised, together with clustering (to be described later). A straightforward approach to association learning is to break the problem into a learning problems, a being the number of attributes. In each new problem, one attribute is taken as a target (dependent) variable, whereas others are treated as independent variables. The type of the learning problem (classification or.
Originate. Most frequently, the types of learning are classified according to the learning complexity, the learning material, and the learning strategy. With respect to the complexity of the learning process we differentiate the following learning types: Imprinting: the simplest form of learning appears when a certain knowledge is imprinted in the learner's memory and after that the knowledge does not change any more. Hatched ducks, for example, follow their mother duck until they grow up. If,.
When the target problem is changing dynamically, thus changing the optimal hypothesis as well. It could be expected that the incremental learning process converges to the optimal hypothesis for a given problem, and that after some time, the hypothesis won't change in spite of new input data. Unfortunately, the learning algorithm never knows whether the optimal hypothesis has been learnt, because at any time new data can be contradictory to the current hypothesis. In practice, however, often after.
Boosting; for decision trees frequently random forests are used. When several hypotheses are built they need to be combined into the final hypothesis (classifier or regressor) by the principle of multiple explanations. As the conditional hypothesis probabilities P(H\E, B) from Equation (3.9) are usually not known, some suitable method for combining hypotheses needs to be used. In the following sections we review some popular approaches to hypothesis combination. 92 3.5.1 Machine Learning.
Possible in order to maximize the number of bits (errors) it is able to correct. In Table 3.6 the minimal Sec. 3.6] Combining several ML algorithms 101 (and also the maximal) difference between each pair of rows is 8 bits. This ensures the correction of at most four errors. Column resolution: each code bit stands for one two-class classification problem. All two-class problems must be uncorrelated; therefore all columns must be different. And, since complementing a column keeps a two-class.