1 IQ estimation for accurate time-series classification. Krisztian Buza, Alexandros Nanopoulos, Lars Schmidt-Thieme. CIDM 2011, 216-223. Web SearchBibTeXDownload
2 Time-Series Classification Based on Individualised Error Prediction. Krisztian Buza, Alexandros Nanopoulos, Lars Schmidt-Thieme. CSE 2010, 48-54. Web SearchBibTeXDownload
3 Individualized Error Estimation for Classification and Regression Models. Krisztian Buza, Alexandros Nanopoulos, Lars Schmidt-Thieme. GfKl 2010, 183-191. Web SearchBibTeXDownload
4 Motif-Based Classification of Time Series with Bayesian Networks and SVMs. Krisztian Buza, Lars Schmidt-Thieme. GfKl 2008, 105-114. Web SearchBibTeXDownload
5 Graph-Based Model-Selection Framework for Large Ensembles. Krisztian Buza, Alexandros Nanopoulos, Lars Schmidt-Thieme. HAIS (1) 2010, 557-564. Web SearchBibTeXDownload
6 Fusion of Similarity Measures for Time Series Classification. Krisztian Buza, Alexandros Nanopoulos, Lars Schmidt-Thieme. HAIS (2) 2011, 253-261. Web SearchBibTeXDownload
7 Fast Classification of Electrocardiograph Signals via Instance Selection. Krisztian Buza, Alexandros Nanopoulos, Lars Schmidt-Thieme, Julia Koller. HISB 2011, 9-16. Web SearchBibTeXDownload
8 Scalable Event-Based Clustering of Social Media Via Record Linkage Techniques. Timo Reuter, Philipp Cimiano, Lucas Drumond, Krisztian Buza, Lars Schmidt-Thieme. ICWSM 2011. Web SearchBibTeXDownload
9 Folksonomy-Based Collabulary Learning. Leandro Balby Marinho, Krisztian Buza, Lars Schmidt-Thieme. International Semantic Web Conference 2008, 261-276. Web SearchBibTeXDownload
10 GRAMOFON: General model-selection framework based on networks. Krisztian Buza, Alexandros Nanopoulos, Tomás Horváth, Lars Schmidt-Thieme. Neurocomputing (75): 163-170 (2012). Web SearchBibTeXDownload
11 INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification. Krisztian Buza, Alexandros Nanopoulos, Lars Schmidt-Thieme. PAKDD (2) 2011, 149-160. Web SearchBibTeXDownload
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