Vital: Visual tracking via adversarial learning
The tracking-by-detection framework consists of two stages, ie, drawing samples around the
target object in the first stage and classifying each sample as the target object or as …
target object in the first stage and classifying each sample as the target object or as …
Crossmine: Efficient classification across multiple database relations
Most of today's structured data is stored in relational data-bases. Such a database consists
of multiple relations that are linked together conceptually via entity-relationship links in the …
of multiple relations that are linked together conceptually via entity-relationship links in the …
Discovering unbounded episodes in sequential data
G Casas-Garriga - European Conference on Principles of Data Mining …, 2003 - Springer
One basic goal in the analysis of time-series data is to find frequent interesting episodes, ie,
collections of events occurring frequently together in the input sequence. Most widely-known …
collections of events occurring frequently together in the input sequence. Most widely-known …
Transforming graph data for statistical relational learning
Relational data representations have become an increasingly important topic due to the
recent proliferation of network datasets (eg, social, biological, information networks) and a …
recent proliferation of network datasets (eg, social, biological, information networks) and a …
A multi-relational decision tree learning algorithm–implementation and experiments
A Atramentov, H Leiva, V Honavar - International Conference on Inductive …, 2003 - Springer
We describe an efficient implementation (MRDTL-2) of the Multi-relational decision tree
learning (MRDTL) algorithm [23] which in turn was based on a proposal by Knobbe et …
learning (MRDTL) algorithm [23] which in turn was based on a proposal by Knobbe et …
Mr-SBC: a multi-relational naive bayes classifier
In this paper we propose an extension of the naïve Bayes classification method to the multi-
relational setting. In this setting, training data are stored in several tables related by foreign …
relational setting. In this setting, training data are stored in several tables related by foreign …
Data mining from multiple heterogeneous relational databases using decision tree classification
T Mehenni, A Moussaoui - Pattern Recognition Letters, 2012 - Elsevier
Nowadays, the expansion of computer networks and the diversity of data sources require
new data mining approaches in multi-database systems. We propose a classification …
new data mining approaches in multi-database systems. We propose a classification …
Multi relational data mining approaches: A data mining technique
N Padhy, R Panigrahi - arXiv preprint arXiv:1211.3871, 2012 - arxiv.org
The multi relational data mining approach has developed as an alternative way for handling
the structured data such that RDBMS. This will provides the mining in multiple tables directly …
the structured data such that RDBMS. This will provides the mining in multiple tables directly …
Spatial associative classification: propositional vs structural approach
Abstract In Spatial Data Mining, spatial dimension adds a substantial complexity to the data
mining task. First, spatial objects are characterized by a geometrical representation and …
mining task. First, spatial objects are characterized by a geometrical representation and …
Concept discovery on relational databases: new techniques for search space pruning and rule quality improvement
Y Kavurucu, P Senkul, IH Toroslu - Knowledge-Based Systems, 2010 - Elsevier
Multi-relational data mining has become popular due to the limitations of propositional
problem definition in structured domains and the tendency of storing data in relational …
problem definition in structured domains and the tendency of storing data in relational …