Appice, Annalisa
Annalisa Appice
VIAF ID: 300247556 (Personal)
Permalink: http://viaf.org/viaf/300247556
Preferred Forms
- 100 0 _ ‡a Annalisa Appice
- 100 1 _ ‡a Appice, Annalisa
- 100 1 _ ‡a Appice, Annalisa
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- 100 1 _ ‡a Appice, Annalisa
- 100 1 _ ‡a Appice, Annalisa
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- 100 1 _ ‡a Appice, Annalisa
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4xx's: Alternate Name Forms (3)
5xx's: Related Names (4)
- 551 _ _ ‡a Bari ‡4 ortw ‡4 https://d-nb.info/standards/elementset/gnd#placeOfActivity
- 510 2 _ ‡a ECML PKDD 2012 Bristol
- 510 2 _ ‡a Università degli studi di Bari Aldo Moro ‡4 affi ‡4 https://d-nb.info/standards/elementset/gnd#affiliation ‡e Affiliation
- 510 2 _ ‡a Università degli Studi di Bari Aldo Moro
Works
Title | Sources |
---|---|
Approximate Frequent Itemset Discovery from Data Stream | |
A Business Intelligence Solution for Monitoring Efficiency of Photovoltaic Power Plants | |
Collective Inference for Handling Autocorrelation in Network Regression | |
Complex objects ranking | |
Complex pattern mining : new challenges, methods and applications | |
Data Mining Techniques in Sensor Networks : Summarization, Interpolation and Surveillance | |
Dealing with temporal and spatial correlations to classify outliers in geophysical data streams | |
Discovering Emerging Patterns for Anomaly Detection in Network Connection Data | |
Discovering process models through relational disjunctive patterns mining | |
Discovery Science : 23rd International Conference, DS 2020, Thessaloniki, Greece, October 19-21, 2020, Proceedings | |
Empowering a GIS with inductive learning capabilities: the case of INGENS | |
Enhancing Regression Models with Spatio-temporal Indicator Additions | |
Foundations of Intelligent Systems : 23rd International Symposium, ISMIS 2017, Warsaw, Poland, June 26-29, 2017, Proceedings | |
A Grid-Based Multi-relational Approach to Process Mining | |
An Integrated Platform for Spatial Data Mining within a GIS Environment | |
Integrating Cluster Analysis to the ARIMA Model for Forecasting Geosensor Data | |
An Intelligent System for Real Time Fault Detection in PV Plants | |
An Intelligent Technique for Forecasting Spatially Correlated Time Series | |
Learning and Transferring Geographically Weighted Regression Trees across Time | |
Leveraging the power of local spatial autocorrelation in geophysical interpolative clustering | |
Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II | |
MBlab: Molecular Biodiversity Laboratory | |
Mining geospatial data in a transductive setting | |
Mining Model Trees from Spatial Data | |
Mining Relational Association Rules for Propositional Classification | |
New Frontiers in Mining Complex Patterns : 6th International Workshop, NFMCP 2017, Held in Conjunction with ECML-PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Revised Selected Papers | |
Predictive Regional Trees to Supplement Geo-Physical Random Fields | |
Proceedings of the 2nd International Workshoop on Knowledge Discovery in Inductive Databases | |
Proceedings of the Sixth International Workshop on Multi-Relational Data Mining MRDM'07, September 17, 2007, Warsaw, Poland | |
Process Mining to Forecast the Future of Running Cases | |
Relational Frequent Patterns Mining for Novelty Detection from Data Streams | |
Relational Mining in Spatial Domains: Accomplishments and Challenges | |
Space-Time Roll-up and Drill-down into Geo-Trend Stream Cubes | |
STARDUST: A Novel Process Mining approach to Discover Evolving Models From trace Streams | |
Stepwise Induction of Logistic Model Trees | |
Summarizing numeric spatial data streams by trend cluster discovery | |
Top-down induction of model trees with regression and splitting nodes | |
Transductive learning for spatial regression with co-training | |
Transductive Relational Classification in the Co-training Paradigm | |
Trend cluster based compression of geographically distributed data streams | |
Using Geographic Cost Functions to Discover Vessel Itineraries from AIS Messages | |
Using trend clusters for spatiotemporal interpolation of missing data in a sensor network |