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Handling KDD process changes by incremental replanning [chapter]

Ning Zhong, Chunnian Liu, Yoshitsugu Kakemoto, Setsuo Ohsuga
1998 Lecture Notes in Computer Science  
With the issue being properly handled, the GLS system is more complete in KDD process modeling, and more flexible and robust for practical use.  ...  Within the framework of our GLS (Global Learning Scheme) system that is a multi-strategy and cooperative KDD (Knowledge Discovery in Databases) system, this paper reports new research progress, by addressing  ...  -Because of the hierarchical planning, the KDD process plan has a hierarchical structure.  ... 
doi:10.1007/bfb0094811 fatcat:qn4jkofqz5gh7bfjmupsxorsxu

Road Network Modeling with Layered Abstraction for Path Discovery in Vehicle Navigation Systems

Jeng-Shyang Pan, Chen Wang, Tien-Wen Sung
2016 Journal of Information Hiding and Multimedia Signal Processing  
This paper focuses on the road network modeling to speed up real time path discovery in dynamic and complex road networks.  ...  Static and dynamic information of the real time road network conditions is concerned in the process of network abstraction.  ...  The authors would like to thank Fuzhou Investigation and Surveying Institute, and Fujian Provincial Key Laboratory of Big Data Mining and Applications in China for the support of this research.  ... 
dblp:journals/jihmsp/0001W016 fatcat:r4f5jj5hpfagjbpi2zop22ifp4

Hybrid Hierarchical Learning from Dynamic Scenes [chapter]

Prithwijit Guha, Pradeep Vaghela, Pabitra Mitra, K. S. Venkatesh, Amitabha Mukerjee
2005 Lecture Notes in Computer Science  
The agent/event discovery is performed at the next higher layer by processing the agent features, status history and trajectory.  ...  The work proposes a hierarchical architecture for learning from dynamic scenes at various levels of knowledge abstraction.  ...  Raw visual data is processed at the lowest level to yield a perception of the background model of the scene.  ... 
doi:10.1007/11590316_28 fatcat:a52zlh4wzfflxnrks55k7riopu

Using Topic Modelling Algorithms for Hierarchical Activity Discovery [chapter]

Eoin Rogers, John D. Kelleher, Robert J. Ross
2016 Advances in Intelligent Systems and Computing  
Activity discovery is the unsupervised process of discovering patterns in data produced from sensor networks that are monitoring the behaviour of human subjects.  ...  Improvements in activity discovery may simplify the training of activity recognition models by enabling the automated annotation of datasets and also the construction of systems that can detect and highlight  ...  While this hierarchical process is the primary contribution of this paper, we first discuss the non-hierarchical aspects of the system.  ... 
doi:10.1007/978-3-319-40114-0_5 fatcat:7tfdbjmtbzb3dolqokflcwubfu

Distributed Prediction and Hierarchical Knowledge Discovery by ARTMAP Neural Networks [chapter]

Gail A. Carpenter
2003 Lecture Notes in Computer Science  
ART networks function both as models of human cognitive information processing [1,2,3] and as neural systems for technology transfer [4] .  ...  It uses [ART] to cluster binary templates of aeroplane parts in a complex hierarchical network that covers over 100,000 items, grouped into thousands of self-organised clusters.  ...  expert systems for multi-level object grouping, information fusion, and discovery of hierarchical knowledge structures.  ... 
doi:10.1007/978-3-540-45224-9_1 fatcat:fxxuxkjnhjbh5bduri2fd3c6tu

TeXDYNA: Hierarchical Reinforcement Learning in Factored MDPs [chapter]

Olga Kozlova, Olivier Sigaud, Christophe Meyer
2010 Lecture Notes in Computer Science  
In this paper, we present TeXDYNA, an algorithm designed to solve large reinforcement learning problems with unknown structure by integrating hierarchical abstraction techniques of Hierarchical Reinforcement  ...  Learning and factorization techniques of Factored Reinforcement Learning.  ...  This upward update guarantees that the model of transitions includes the changes that occur during the incremental learning process.  ... 
doi:10.1007/978-3-642-15193-4_46 fatcat:5moziwlpdnantdq4dp5qim4k34

A New Paradigm for Groundwater Modeling [chapter]

Shu-Guang Li, Qun Liu
2008 Studies in Computational Intelligence  
stochastic and hierarchical modeling, and applications.  ...  tracking, plume modeling, hierarchical subscale modeling, stochastic modeling, monitoring, and mass balance analyses.  ...  It presents serious obstacles in the process of professional investigation and scientific discovery.  ... 
doi:10.1007/978-3-540-75384-1_2 fatcat:6rs2dutwcbhldkliy5hruf2w2m

The Inscrutability of Reference [chapter]

Donald Davidson
2001 Inquiries into Truth and Interpretation  
The addition of constraints allows users to incorporate domain expertise into the clustering process by explicitly specifying what are desirable properties in a clustering solution.  ...  We cover approaches that make use of constraints for partitional and hierarchical algorithms to both enforce the constraints or to learn a distance function from the constraints. REFERENCES ABE, N.  ...  A view of the EM algorithm that justifies incremental, sparse, and other variants. In Learning in Graphical Models, M. I. Jordan, Ed. MIT Press, 355-368. PELLEG, D. AND BARAS, D. 2007.  ... 
doi:10.1093/0199246297.003.0016 fatcat:m527offlcngibl3t55hzie2raq

The Inscrutability of Reference

Donald Davidson
1979 Southwestern Journal of Philosophy  
The addition of constraints allows users to incorporate domain expertise into the clustering process by explicitly specifying what are desirable properties in a clustering solution.  ...  We cover approaches that make use of constraints for partitional and hierarchical algorithms to both enforce the constraints or to learn a distance function from the constraints. REFERENCES ABE, N.  ...  A view of the EM algorithm that justifies incremental, sparse, and other variants. In Learning in Graphical Models, M. I. Jordan, Ed. MIT Press, 355-368. PELLEG, D. AND BARAS, D. 2007.  ... 
doi:10.5840/swjphil197910229 fatcat:uafnl7hhynaf5czc2xoj66lgja

Dynamical hierarchical self‐organization of harmonic and motivic musical categories

Ricard Marxer, Piotr Holonowicz, Amaury Hazan, Hendrik Purwins
2008 Journal of the Acoustical Society of America  
We introduce a generic model of emergence of musical categories during the listening process. The model is based on a preprocessing and a categorization module.  ...  The potential of the model is exemplified by exposing it to three different datasets resulting in musical categories of scales, motives, and harmonies consistent with music theory.  ...  The performance element quantifies the inference ability of the model, and the environment evaluation tests the incremental nature of the model.  ... 
doi:10.1121/1.2935489 fatcat:djztltyqvjdenjt5wo4y45zqb4

Associations Between Resting State Functional Connectivity and a Hierarchical Dimensional Structure of Psychopathology in Middle Childhood [article]

Nicole Karcher, Giorgia Michelini, Roman Kotov, Deanna Barch
2020 bioRxiv   pre-print
Conclusion: The hierarchical structure of psychopathology showed replicable links to RSFC alterations in middle childhood.  ...  Analyses were first conducted in a discovery dataset (n=3790) with significant associations examined in a replication dataset (n=3791).  ...  and externalizing) did not account for a significant increment in variance of any of the RSFC metrics over the model with covariates + p-factor in the discovery dataset at FDR threshold (Supplemental  ... 
doi:10.1101/2020.04.28.065086 fatcat:7csceenbzfdw3mlo76nd5p4c2i

PERCEPTION-BASED ADVANCED DESCRIPTION OF ABSTRACT MUSICAL CONTENT

O. LARTILLOT
2003 Digital Media Processing for Multimedia Interactive Services  
Through an incremental detection of approximate repetitions of patterns, a conceptual network is built on the score.  ...  Musical Pattern Discovery aims at inducing pertinent structures within abstract musical description, with the view to developing advanced musical content-based browsing.  ...  Acknowledgments This project is carried out in the context of my PhD, supervised by Emmanuel Saint-James (LIP6, Paris VI) and Gérard Assayag (Musical Representations, Ircam).  ... 
doi:10.1142/9789812704337_0058 fatcat:v5brobfl7bfl3bcu5fzuz4qvcy

An Improved Process Discovery Approach Based on the Markov Transition Matrix

Hong LI, Hao GAO
2017 DEStech Transactions on Computer Science and Engineering  
process.The paperaims to enhance the flexibility and adaptability of the process discovery algorithm.  ...  The paperfirst analyses the process patterns using hierarchical structure, then proposes an improved multi-step process discovery approach based on the first-order Markov transition matrix.  ...  Process mining can be divided into three types: process discovery, conformance checking and process enhancement.  ... 
doi:10.12783/dtcse/csma2017/17381 fatcat:vj6x6yvk2jaebfsxjceugjeqma

Interactive GSOM-Based Approaches for Improving Biomedical Pattern Discovery and Visualization [chapter]

Haiying Wang, Francisco Azuaje, Norman Black
2004 Lecture Notes in Computer Science  
These models provided the basis for the implementation of hierarchical clustering, cluster validity assessment and a method for monitoring learning processes (cluster formation).  ...  Clustering and pattern visualization models were based on the adaptation of a self-adaptive neural network known as Growing Self Organizing Maps.  ...  The GSOM incrementally grows new neurons to achieve a better representation of the input data.  ... 
doi:10.1007/978-3-540-30497-5_87 fatcat:mufrcvsyqjavhdqx74vd2dkbjy

Research on Hotspot Discovery in Internet Public Opinions Based on Improved -Means

Gensheng Wang
2013 Computational Intelligence and Neuroscience  
An improved -means algorithm for hotspot discovery in internet public opinions is presented based on the analysis of existing defects and calculation principle of original -means algorithm.  ...  Finally, the experimental results verify that the improved algorithm can improve the clustering stability and classification accuracy of hotspot discovery in internet public opinions when used in practice  ...  With clustering, the similar text is integrated into a cluster gradually and the hierarchical clustering is able to automatically generate different hierarchical clustering model.  ... 
doi:10.1155/2013/230946 pmid:24106496 pmcid:PMC3782808 fatcat:22jrjpqqxjdlzmnitvr6wnkio4
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