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Maintaining knowledge about temporal intervals
 COMMUNICATION OF ACM
, 1983
"... The problem of representing temporal knowledge arises in many areas of computer science. In applications in which such knowledge is imprecise or relative, current representations based on date lines or time instants are inadequate. An intervalbased temporal logic is introduced, together WiUl a comp ..."
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Cited by 2942 (13 self)
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The problem of representing temporal knowledge arises in many areas of computer science. In applications in which such knowledge is imprecise or relative, current representations based on date lines or time instants are inadequate. An intervalbased temporal logic is introduced, together WiUl a
A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
, 1989
"... The exact form of a gradientfollowing learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks. These algorithms have: (1) the advantage that they do not require a precis ..."
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Cited by 534 (4 self)
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The exact form of a gradientfollowing learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks. These algorithms have: (1) the advantage that they do not require a
K.B.: MultiInterval Discretization of ContinuousValued Attributes for Classication Learning. In:
 IJCAI.
, 1993
"... Abstract Since most realworld applications of classification learning involve continuousvalued attributes, properly addressing the discretization process is an important problem. This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuousvalued a ..."
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Cited by 832 (7 self)
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Abstract Since most realworld applications of classification learning involve continuousvalued attributes, properly addressing the discretization process is an important problem. This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuous
The synchronous dataflow programming language LUSTRE
 Proceedings of the IEEE
, 1991
"... This paper describes the language Lustre, which is a dataflow synchronous language, designed for programming reactive systems  such as automatic control and monitoring systems  as well as for describing hardware. The dataflow aspect of Lustre makes it very close to usual description tools in t ..."
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Cited by 646 (50 self)
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formalism is very similar to temporal logics. This allows the language to be used for both writing programs and expressing program properties, which results in an original program verification methodology. 1 Introduction Reactive systems Reactive systems have been defined as computing systems which
Video google: A text retrieval approach to object matching in videos
 In ICCV
, 2003
"... We describe an approach to object and scene retrieval which searches for and localizes all the occurrences of a user outlined object in a video. The object is represented by a set of viewpoint invariant region descriptors so that recognition can proceed successfully despite changes in viewpoint, ill ..."
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Cited by 1636 (42 self)
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, illumination and partial occlusion. The temporal continuity of the video within a shot is used to track the regions in order to reject unstable regions and reduce the effects of noise in the descriptors. The analogy with text retrieval is in the implementation where matches on descriptors are pre
HyTech: A Model Checker for Hybrid Systems
 Software Tools for Technology Transfer
, 1997
"... A hybrid system is a dynamical system whose behavior exhibits both discrete and continuous change. A hybrid automaton is a mathematical model for hybrid systems, which combines, in a single formalism, automaton transitions for capturing discrete change with differential equations for capturing conti ..."
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Cited by 473 (6 self)
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A hybrid system is a dynamical system whose behavior exhibits both discrete and continuous change. A hybrid automaton is a mathematical model for hybrid systems, which combines, in a single formalism, automaton transitions for capturing discrete change with differential equations for capturing
A Growing Neural Gas Network Learns Topologies
 Advances in Neural Information Processing Systems 7
, 1995
"... An incremental network model is introduced which is able to learn the important topological relations in a given set of input vectors by means of a simple Hebblike learning rule. In contrast to previous approaches like the "neural gas" method of Martinetz and Schulten (1991, 1994), this m ..."
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Cited by 401 (5 self)
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), this model has no parameters which change over time and is able to continue learning, adding units and connections, until a performance criterion has been met. Applications of the model include vector quantization, clustering, and interpolation. 1 INTRODUCTION In unsupervised learning settings only input
Temporal databases
 IEEE Computer
, 1986
"... A temporal database (see Temporal Database) contains timevarying data. Time is an important aspect of all realworld phenomena. Events occur at specific points in time; objects and the relationships among objects exist over time. The ability to model this temporal dimension of the real world is ess ..."
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Cited by 309 (45 self)
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A temporal database (see Temporal Database) contains timevarying data. Time is an important aspect of all realworld phenomena. Events occur at specific points in time; objects and the relationships among objects exist over time. The ability to model this temporal dimension of the real world
OnLine QLearning Using Connectionist Systems
, 1994
"... Reinforcement learning algorithms are a powerful machine learning technique. However, much of the work on these algorithms has been developed with regard to discrete finitestate Markovian problems, which is too restrictive for many realworld environments. Therefore, it is desirable to extend these ..."
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Cited by 381 (1 self)
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these methods to high dimensional continuous statespaces, which requires the use of function approximation to generalise the information learnt by the system. In this report, the use of backpropagation neural networks (Rumelhart, Hinton and Williams 1986) is considered in this context. We consider a number
Image Representation Using 2D Gabor Wavelets
 IEEE Trans. Pattern Analysis and Machine Intelligence
, 1996
"... This paper extends to two dimensions the frame criterion developed by Daubechies for onedimensional wavelets, and it computes the frame bounds for the particular case of 2D Gabor wavelets. Completeness criteria for 2D Gabor image representations are important because of their increasing role in man ..."
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Cited by 375 (4 self)
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This paper extends to two dimensions the frame criterion developed by Daubechies for onedimensional wavelets, and it computes the frame bounds for the particular case of 2D Gabor wavelets. Completeness criteria for 2D Gabor image representations are important because of their increasing role
Results 1  10
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