Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Selected papers of the 9th annual ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
The nature of statistical learning theory
The nature of statistical learning theory
Database management systems
The DEDALE system for complex spatial queries
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
The MLPQ/GIS constraint database system
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A relational model of data for large shared data banks
Communications of the ACM
A framework for data mining and KDD
Proceedings of the 2002 ACM symposium on Applied computing
Introduction to constraint databases
Introduction to constraint databases
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
Machine Learning
The 3W Model and Algebra for Unified Data Mining
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Classes of Spatio-Temporal Objects and their Closure Properties
Annals of Mathematics and Artificial Intelligence
Spatiotemporal reasoning about epidemiological data
Artificial Intelligence in Medicine
Reclassification of Linearly Classified Data Using Constraint Databases
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
Expert Systems with Applications: An International Journal
Efficient Learning from Massive Spatial-Temporal Data through Selective Support Vector Propagation
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Efficient MaxCount and threshold operators of moving objects
Geoinformatica
Constraint Databases
Hi-index | 0.00 |
Data classification is usually based on measurements recorded at the same time. This paper considers temporal data classification where the input is a temporal database that describes measurements over a period of time in history while the predicted class is expected to occur in the future. We describe a new temporal classification method that improves the accuracy of standard classification methods. The benefits of the method are tested on weather forecasting using the meteorological database from the Texas Commission on Environmental Quality.