Results 11  20
of
577
Towards Instrument Segmentation for Music Content Description: A critical review of instrument classification techniques
 International Symposium on Music Information Retrieval
, 2000
"... this paper we concentrate on reviewing the different techniques that have been so far proposed for automatic classification of musical instruments. As most of the techniques to be discussed are usable only in "solo" performances we will evaluate their applicability to the more complex case ..."
Abstract

Cited by 41 (5 self)
 Add to MetaCart
(Show Context)
this paper we concentrate on reviewing the different techniques that have been so far proposed for automatic classification of musical instruments. As most of the techniques to be discussed are usable only in "solo" performances we will evaluate their applicability to the more complex case of describing sound mixes. We conclude this survey discussing the necessity of developing new strategies for classifying sound mixes without a priori separation of sound sources
A decision theoretic framework for approximating concepts
 International Journal of Manmachine Studies
, 1992
"... This paper explores the implications of approximating a concept based on the Bayesian decision procedure, which provides a plausible unification of the fuzzy set and rough set approaches for approximating a concept. We show that if a given concept is approximated by one set, the same result given by ..."
Abstract

Cited by 37 (20 self)
 Add to MetaCart
(Show Context)
This paper explores the implications of approximating a concept based on the Bayesian decision procedure, which provides a plausible unification of the fuzzy set and rough set approaches for approximating a concept. We show that if a given concept is approximated by one set, the same result given by the αcut in the fuzzy set theory is obtained. On the other hand, if a given concept is approximated by two sets, we can derive both the algebraic and probabilistic rough set approximations. Moreover, based on the well known principle of maximum (minimum) entropy, we give a useful interpretation of fuzzy intersection and union. Our results enhance the understanding and broaden the applications of both fuzzy and rough sets. 1.
Information granulation and rough set approximation
 International Journal of Intelligent Systems
, 2001
"... Information granulation and concept approximation are some of the fundamental issues of granular computing. Granulation of a universe involves grouping of similar elements into granules to form coarsegrained views of the universe. Approximation of concepts, represented by subsets of the universe, d ..."
Abstract

Cited by 37 (17 self)
 Add to MetaCart
(Show Context)
Information granulation and concept approximation are some of the fundamental issues of granular computing. Granulation of a universe involves grouping of similar elements into granules to form coarsegrained views of the universe. Approximation of concepts, represented by subsets of the universe, deals with the descriptions of concepts using granules. In the context of rough set theory, this paper examines the two related issues. The granulation structures used by standard rough set theory and the corresponding approximation structures are reviewed. Hierarchical granulation and approximation structures are studied, which results in stratified rough set approximations. A nested sequence of granulations induced by a set of nested equivalence relations leads to a nested sequence of rough set approximations. A multilevel granulation, characterized by a special class of equivalence relations, leads to a more general approximation structure. The notion of neighborhood systems is also explored. 1
A Partition Model of Granular Computing
 LNCS Transactions on Rough Sets
, 2004
"... There are two objectives of this chapter. One objective is to examine the basic principles and issues of granular computing. We focus on the tasks of granulation and computing with granules. From semantic and algorithmic perspectives, we study the construction, interpretation, and representation ..."
Abstract

Cited by 32 (9 self)
 Add to MetaCart
(Show Context)
There are two objectives of this chapter. One objective is to examine the basic principles and issues of granular computing. We focus on the tasks of granulation and computing with granules. From semantic and algorithmic perspectives, we study the construction, interpretation, and representation of granules, as well as principles and operations of computing and reasoning with granules. The other objective is to study a partition model of granular computing in a settheoretic setting. The model is based on the assumption that a finite set of universe is granulated through a family of pairwise disjoint subsets. A hierarchy of granulations is modeled by the notion of the partition lattice.
Semantic Translation Based on Approximate ReClassification
, 2000
"... We present a knowledgebased approachtointelligent information integration based on a reclassi#cation of information entities in a new context. We identify the needs of approaches performing semantic translation, weintroduce a uniform description of contexts and discuss a naive classi#cation a ..."
Abstract

Cited by 32 (9 self)
 Add to MetaCart
We present a knowledgebased approachtointelligent information integration based on a reclassi#cation of information entities in a new context. We identify the needs of approaches performing semantic translation, weintroduce a uniform description of contexts and discuss a naive classi#cation approach. We argue that this approach makes unrealistic assumptions about the absence of uncertainty. Toovercome this problem we discuss several approximate classi#cation approaches and their use for information integration. Therebywe address symbolic as well as numeric approaches for uncertainty handling. We sumarize with a description of an actual application area and a discussion of open research topics.
A Formal Model of the Process of Wayfinding in Built Environments
 In C. Freksa & D.M. Mark (Eds.), Spatial
, 1999
"... . Previous recent research on human wayfinding has focused primarily on mental representations rather than processes of wayfinding. This paper presents a formal model of some aspects of the process of wayfinding, where appropriate elements of human perception and cognition are formally realized ..."
Abstract

Cited by 29 (4 self)
 Add to MetaCart
. Previous recent research on human wayfinding has focused primarily on mental representations rather than processes of wayfinding. This paper presents a formal model of some aspects of the process of wayfinding, where appropriate elements of human perception and cognition are formally realized using image schemata and affordances. The goaldriven reasoning chain that leads to action begins with incomplete and imprecise knowledge derived from imperfect observations of space. Actions result in further observations, derived knowledge and, recursively, further actions, until the goal is achieved or the wayfinder gives up. This paper gives a formalization of this process, using a modal extension to classical propositional logic to represent incomplete knowledge. Both knowledge and action are represented through a wayfinding graph. A special case of wayfinding in a building, that is finding one's way through an airport, is used to demonstrate the formal model. Keywords. Wayfind...
Useful Feature Subsets and Rough Set Reducts
, 1994
"... In supervised classification learning, one attempts to induce a classifier that correctly predicts the label of novel instances. We demonstrate that by choosing a useful subset of features for the indiscernibility relation, an induction algorithm based on simple decision table can have high predicti ..."
Abstract

Cited by 26 (4 self)
 Add to MetaCart
In supervised classification learning, one attempts to induce a classifier that correctly predicts the label of novel instances. We demonstrate that by choosing a useful subset of features for the indiscernibility relation, an induction algorithm based on simple decision table can have high prediction accuracy on artificial and realworld datasets. We show that useful feature subsets are not necessarily maximal independent sets (relative reducts) with respect to the label, and that, in practical situations, using a subset of the relative core features may lead to superior performance. 1 Introduction In supervised classification learning, one is given a training set containing labelled instances (examples) . Each labelled instance contains a list of feature values (attribute values) and a discrete label value. The induction task is to build a classifier that will correctly predict the label of novel instances. Common classifiers are decision trees, neural networks, and nearestneighbor...
Rough Set Approach To KnowledgeBased Decision Support
 European Journal of Operational Research
, 1995
"... . The rough set concept is a new mathematical approach to imprecision, vagueness and uncertainty. To some extend it overlaps with fuzzy set theory and evidence theory  nevertheless the rough set theory can be viewed in its own rights, as an independent discipline. Many reallife applications of th ..."
Abstract

Cited by 24 (0 self)
 Add to MetaCart
(Show Context)
. The rough set concept is a new mathematical approach to imprecision, vagueness and uncertainty. To some extend it overlaps with fuzzy set theory and evidence theory  nevertheless the rough set theory can be viewed in its own rights, as an independent discipline. Many reallife applications of the theory have proved its practical usefulness. The paper presents the basic assumptions underlying the rough sets philosophy, gives its fundamental concepts and discusses briefly some areas of applications, in particular in decision support. Finally further problems are shortly outlined. 1 Introduction 1.1 Basic Philosophy The rough set concept proposed by the author in [5] is a new mathematical approach to imprecision, vagueness and uncertainty. The rough set philosophy is founded on the assumption that with every objects of the universe of discourse we associate some information (data, knowledge). E.g. if objects are patients suffering from a certain disease, symptoms of the disease form...
Multicriteria classification and sorting methods: A literature review
 European Journal of Operational Research
"... The assignment of alternatives (observations/objects) into predefined homogenous groups is a problem of major practical and research interest. This type of problem is referred to as classification or sorting, depending on whether the groups are nominal or ordinal. Methodologies for addressing classi ..."
Abstract

Cited by 23 (0 self)
 Add to MetaCart
(Show Context)
The assignment of alternatives (observations/objects) into predefined homogenous groups is a problem of major practical and research interest. This type of problem is referred to as classification or sorting, depending on whether the groups are nominal or ordinal. Methodologies for addressing classification and sorting problems have been developed from a variety of research disciplines, including statistics/econometrics, artificial intelligent and operations research. The objective of this paper is to review the research conducted on the framework of the multicriteria decision aiding (MCDA). The review covers different forms of MCDA classification/sorting models, different aspects of the model development process, as well as realworld applications of MCDA classification/sorting techniques and their software