Travelled to:1 × Canada
1 × Italy
1 × Switzerland
4 × Germany
Collaborated with:M.Ceci D.Malerba C.Loglisci N.Barile S.Rawles P.A.Flach P.Guccione A.Ciampi A.Muolo H.L.Viktor E.Paquet H.Guo C.Caruso F.Fumarola C.Valente
Talks about:relat (4) transduct (3) data (3) regress (2) train (2) learn (2) mine (2) everywher (1) paradigm (1) interpol (1)
Person: Annalisa Appice
DBLP: Appice:Annalisa
Contributed to:
2012
2012201020092007
20042003Wrote 8 papers:
- MLDM-2012-CeciAVMPG #classification #paradigm #relational
- Transductive Relational Classification in the Co-training Paradigm (MC, AA, HLV, DM, EP, HG), pp. 11–25.
- SAC-2012-GuccioneCAMM #clustering #network
- Trend cluster based interpolation everywhere in a sensor network (PG, AC, AA, DM, AM), pp. 827–828.
- SAC-2010-AppiceCM #learning
- Transductive learning for spatial regression with co-training (AA, MC, DM), pp. 1065–1070.
- SAC-2010-CeciALM #approach #data mining #mining #ranking #relational
- Complex objects ranking: a relational data mining approach (MC, AA, CL, DM), pp. 1071–1077.
- MLDM-2009-CeciALCFVM #data type #detection #mining #relational
- Relational Frequent Patterns Mining for Novelty Detection from Data Streams (MC, AA, CL, CC, FF, CV, DM), pp. 427–439.
- MLDM-2007-CeciABM #learning #relational
- Transductive Learning from Relational Data (MC, AA, NB, DM), pp. 324–338.
- ICML-2004-AppiceCRF #multi #problem
- Redundant feature elimination for multi-class problems (AA, MC, SR, PAF).
- MLDM-2003-CeciAM
- Simplification Methods for Model Trees with Regression and Splitting Nodes (MC, AA, DM), pp. 20–34.