The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This chapter has two goals. The first goal is to compare Machine Learning (ML) and Knowledge Discovery in Data (KDD, also often called Data Mining, DM) insisting on how much they actually differ. In order to make my ideas somewhat easier to understand, and as an illustration, I will include a description of several research topics that I find relevant to KDD and to KDD only. The second goal is to...
In concept learning and data mining, a typical objective is to determine concept descriptions or patterns that will classify future data points as correctly as possible. If one can assume that the data contain no noise, then it is desirable that descriptions are complete and consistent with regard to all the data, i.e., they characterize all data points in a given class (positive examples) and no...
Since the field’s inception, most research in machine learning has focused on the problem of supervised induction from labeled training cases. If anything, this trend has been strengthened by the creation of data repositories that, typically, include class information. But this emphasis is misguided if we want to understand the nature of learning in intelligent agents like humans. Clearly, children...
To solve a complex problem, one of the effective general approaches is to decompose it into smaller, less complex and more manageable subproblems. In machine learning, this principle is a foundation for structured induction [44]: instead of learning a single complex classification rule from examples, define a concept hierarchy and learn rules for each of the (sub)concepts. Shapiro [44] used structured...
Current machine learning systems are often distinguished on the basis of their representation, which can either be propositional or first order logic. Systems belonging to the first category are often called attribute value learners, systems of the second category are called relational learners or inductive logic programming systems.
This chapter contains an overview of Case-Based Reasoning (CBR). The main goal is to have a balance between brevity and expressiveness and to provide helpful pointers to literature in the field. To do so, we first describe the CBR types and the CBR cycle, then we briefly review a representative set of systems, next we discuss the connections between CBR and learning. The main part of the chapter analyses...
Genetic algorithms are stochastic search algorithms which act on a population of possible solutions. They are loosely based on the mechanics of population genetics and selection. The potential solutions are encoded as ‘genes’ — strings of characters from some alphabet. New solutions can be produced by ‘mutating’ members of the current population, and by ‘mating’ two solutions together to form a new...
Pattern Recognition (PR) is a fast growing field with applications in many diverse areas such as optical character recognition (OCR), computer – aided diagnosis and speech recognition, to name but a few.
Machine Learning was primarily inspired by human learning. In a branch of Artificial Intelligence scientists tried to build systems that reproduce forms of human learning. Currently the methods that were discovered in this way have been elaborated and are applied to tasks that are not performed by humans at all. For example, one of the most popular applications is the analysis of consumer data to...
With the growing complexity of Machine Learning applications, the need for using integrated or hybrid (or multistrategy) approaches becomes more and more imperative, and an increasing amount of research effort is devoted to this issue. The increasing complexity of applications is not the only reason making multistrategic approaches appealing: as it is well known, no single approach/system can claim...
The process of scientific discovery has long been viewed as the pinnacle of creative thought. Thus, to many people, including some scientists themselves, it seems an unlikely candidate for automation by computer. However, over the past two decades, researchers in artificial intelligence have repeatedly questioned this attitude and attempted to develop intelligent artifacts that replicate the act of...
This chapter presents a summary of the issues discussed during the one day workshop on “Support Vector Machines (SVM) Theory and Applications” organized as part of the Advanced Course on Artificial Intelligence (ACAI ’99) in Chania, Greece [19]. The goal of the chapter is twofold: to present an overview of the background theory and current understanding of SVM, and to discuss the papers presented...
Knowledge discovery in databases (KDD) has become a very attractive discipline both for research and industry within the last few years. Its goal is to extract "pieces" of knowledge or "patterns" from usually very large databases. It portrays a robust sequence of procedures or steps that have to be carried out so as to derive reasonable and understandable results. One...
The undoubted usefulness of present-day information systems is only moderated by the fact that people have to invest substantial effort and training time in order to learn how to use them. Even modern applications with Graphical-User Interfaces (which are considered user-friendly), built-in wizards and on-line context-sensitive help, require a considerable self-training period, thus discouraging most...
As the volume of electronically stored information continues to expand across computer networks, the need for intelligent access to on-line collections of multimedia documents becomes imperative. Examples of such collections are the World Wide Web, digital libraries and enterprise-wide information repositories. Machine learning offers an invaluable corpus of techniques, tools and systems that can...
The purpose of this chapter is to provide an introduction to the field of machine learning techniques for intelligent agents based on the contributions in the workshop of ’Machine Learning and Intelligent Agents’ [20], which was held in conjunction with the Advanced Course on Artificial Intelligence (ACAI ’99) on Machine Learning & Applications, at Chania, Greece.
It is generally recognized that information systems are becoming more complex and, therefore, intelligent user interfaces are needed to improve user interaction with these systems. Furthermore, the exponential growth of the Internet makes it difficult for the users to cope with the huge amount of available on-line information. The challenge that information providers and system engineers face is the...
Data Mining has become a buzzword in industry in recent years. It is something that everyone is talking about but few seem to understand. There are two reasons for this lack of understanding: First is the fact that Data Mining researchers have very diverse backgrounds such as machine learning, psychology and statistics. This means that the research is often based on different methodologies and communication...
Machine Learning (ML) provides methods, techniques, and tools that can help solving diagnostic and prognostic problems in a variety of medical domains. ML is being used for the analysis of the importance of clinical parameters and their combinations for prognosis, e.g. prediction of disease progression, extraction of medical knowledge for outcome research, therapy planning and support, and for the...
The recent developments in the power system area, i.e. the on-going liberalization of the energy markets, the pressing demands for power system efficiency and power quality, the increase of dispersed, renewable generation and the growing number of interconnections and power exchanges among utilities, dictate the need for improvements in the power system planning, operation and control. At the same...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.