Buried pipe localization using an iterative geometric clustering on GPR data
Journal Article
Janning, R., Busche, A., Horváth, T., & Schmidt-Thieme, L. (2014)
Buried pipe localization using an iterative geometric clustering on GPR data. Artificial Intelligence Review, 42(3), 403-425. https://doi.org/10.1007/s10462-013-9410-2
Ground penetrating radar is a non-destructive method to scan the shallow subsurface for detecting buried objects like pipes, cables, ducts and sewers. Such buried objects caus...
Factorization Techniques for Predicting Student Performance
Book Chapter
Thai-Nghe, N., Drumond, L., Horváth, T., Krohn-Grimberghe, A., Nanopoulos, A., & Schmidt-Thieme, L. (2012)
Factorization Techniques for Predicting Student Performance. In O. C. Santos, & J. G. Boticario (Eds.), Educational Recommender Systems and Technologies: Practices and Challenges (129-153). IGI Global. https://doi.org/10.4018/978-1-61350-489-5.ch006
Recommender systems are widely used in many areas, especially in e-commerce. Recently, they are also applied in e-learning for recommending learning objects (e.g. papers) to s...
GRAMOFON: General model-selection framework based on networks
Journal Article
Buza, K., Nanopoulos, A., Horváth, T., & Schmidt-Thieme, L. (2012)
GRAMOFON: General model-selection framework based on networks. Neurocomputing, 75(1), 163-170. https://doi.org/10.1016/j.neucom.2011.02.026
Ensembles constitute one of the most prominent class of hybrid prediction models. One basically assumes that different models compensate each other's errors if one combines th...
A Model of User Preference Learning for Content-Based Recommender Systems
Journal Article
Horvath, T. (2009)
A Model of User Preference Learning for Content-Based Recommender Systems. Computing and Informatics, 28(4), 1001-1029
This paper focuses to a formal model of user preference learning for
content-based recommender systems. First, some fundamental and special requirements to user preference lea...
User Preference Web Search -- Experiments with a System Connecting Web and User
Journal Article
Gurský, P., Horvath, T., Jirásek, J., Krajči, S., Novotny, R., Pribolová, J., …Vojtáš, P. (2009)
User Preference Web Search -- Experiments with a System Connecting Web and User. Computing and Informatics, 28(4), 1001-1033
We present models, methods, implementations and experiments with a system enabling personalized web search for many users with different preferences. The system consists of a ...
Knowledge Processing for Web Search – An Integrated Model and Experiments
Journal Article
Gurský, P., Horvath, T., Jirásek, J., Novotny, R., Pribolová, J., Vaneková, V., & Vojtáš, P. (2008)
Knowledge Processing for Web Search – An Integrated Model and Experiments. Scalable Computing: Practice and Experience, 9(1), 51-59
We propose a model of a middleware system enabling personalized web
search for users with different preferences. We integrate both inductive and deductive tasks to find user p...
Integration of two fuzzy data mining methods
Journal Article
Horvath, T., & Krajči, S. (2004)
Integration of two fuzzy data mining methods. Neural Network World, 14(5), 391-402
The cluster analysis and the formal concept analysis are both used to identify significiant groups of similar objects. Rice & Siff's algorithm for the clustering joins these t...
An ILP model for a monotone graded classification problem
Journal Article
Vojtáš, P., Horvath, T., Krajči, S., & Lencses, R. (2004)
An ILP model for a monotone graded classification problem. Kybernetika, 40(3), 317-332
Motivation for this paper are classification problems in which data can not be clearly divided into positive and negative examples, especially data in which there is a monoton...