ICASSP
2011
IEEE
13 years 4 months ago
2011
IEEE
This paper presents a novel method for audio event classi cation in overlapping conditions. The method is based on Jump Function Kolmogorov (JFK), a stochastic representation, whi...
DATAMINE
1998
14 years 3 days ago
1998
Decision trees have proved to be valuable tools for the description, classi cation and generalizationof data. Work on constructingdecisiontrees from data exists in multiplediscipli...
DM
2006
14 years 13 days ago
2006
Pattern Sturmian words introduced by Kamae and Zamboni KZ1, KZ2] are an analogy of Sturmian words for the maximal pattern complexity instead of the block complexity. So far, two k...
SPIESR
1996
14 years 1 months ago
1996
Two approaches for integrating images into the framework of a database management system are presented. The classi cation approach preprocesses all images and attaches a semantic ...
PDPTA
1996
14 years 1 months ago
1996
This paper presents a taxonomy of parallel and distributed debuggers based on execution replay. Programming of distributed and parallel systems is a complex task. Amongst the many...
DPC
1996
14 years 1 months ago
1996
Retrieving images from very large collections, using image content as a key, is becoming an important problem. Users prefer to ask for pictures using notions of content that are st...
UAI
2000
14 years 1 months ago
2000
Incorporating feature selection into a classi cation or regression method often carries a number of advantages. In this paper we formalize feature selection speci cally from a dis...
BMVC
1997
14 years 1 months ago
1997
This paper describes recent work on the use of regional data extracted from segmented images for use as search keys in an image database query system. The motivation for this work...
ESANN
1998
14 years 1 months ago
1998
Our problem concerns the joint interpretation of UltraSonic and InfraRed measurements provided by a composite proximity sensor, in order to extract geometrical and morphological f...
AGENTS
2001
Springer
14 years 4 months ago
2001
Springer
Multiagent learning di ers from standard machine learning in that most existing learning methods assume that all knowledge is available locally in a single agent. In multiagent sy...