| Nome |
# |
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Spatial autocorrelation and entropy for renewable energy forecasting, file dd9e0c6c-4fb0-1e9c-e053-3a05fe0a45ef
|
127
|
|
CloFAST: closed sequential pattern mining using sparse and vertical id-lists, file dd9e0c6c-3b8b-1e9c-e053-3a05fe0a45ef
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122
|
|
DENCAST: distributed density-based clustering for multi-target regression, file dd9e0c66-9f9a-1e9c-e053-3a05fe0a45ef
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72
|
|
A novel spectral-spatial co-training algorithm for the transductive classification of hyperspectral imagery data, file dd9e0c6b-30a4-1e9c-e053-3a05fe0a45ef
|
69
|
|
Predictive modeling of PV energy production: How to set up the learning task for a better prediction?, file dd9e0c6c-1f26-1e9c-e053-3a05fe0a45ef
|
65
|
|
Multi-type clustering and classification from heterogeneous networks, file dd9e0c6c-7152-1e9c-e053-3a05fe0a45ef
|
64
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|
Using multiple time series analysis for geosensor data forecasting, file dd9e0c6a-e1db-1e9c-e053-3a05fe0a45ef
|
63
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|
Relational mining for discovering changes in evolving networks, file dd9e0c6b-fd2b-1e9c-e053-3a05fe0a45ef
|
58
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Big Data Research in Italy: A Perspective, file dd9e0c64-33de-1e9c-e053-3a05fe0a45ef
|
55
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Active learning via collective inference in network regression problems, file 34d3b78a-88ac-4c76-9c8d-13af9d1622b8
|
54
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|
ComiRNet: A web-based system for the analysis of miRNA-gene regulatory networks, file dd9e0c64-28bd-1e9c-e053-3a05fe0a45ef
|
52
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Segmentation-aided classification of hyperspectral data using spatial dependency of spectral bands, file dd9e0c6b-2368-1e9c-e053-3a05fe0a45ef
|
47
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Mining microscopic and macroscopic changes in network data streams, file dd9e0c6b-e306-1e9c-e053-3a05fe0a45ef
|
47
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Collective regression for handling autocorrelation of network data in a transductive setting, file dd9e0c6c-0d22-1e9c-e053-3a05fe0a45ef
|
38
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|
null, file dd9e0c6a-a939-1e9c-e053-3a05fe0a45ef
|
36
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|
Advanced Programming of Intelligent Social Robots, file dd9e0c67-3165-1e9c-e053-3a05fe0a45ef
|
35
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A novel spectral-spatial co-training algorithm for the transductive classification of hyperspectral imagery data, file dd9e0c64-0fce-1e9c-e053-3a05fe0a45ef
|
18
|
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Using multiple time series analysis for geosensor data forecasting, file dd9e0c64-059b-1e9c-e053-3a05fe0a45ef
|
17
|
|
Predictive modeling of PV energy production: How to set up the learning task for a better prediction?, file dd9e0c6a-04c8-1e9c-e053-3a05fe0a45ef
|
14
|
|
Relational mining for discovering changes in evolving networks, file dd9e0c64-2a25-1e9c-e053-3a05fe0a45ef
|
13
|
|
Collective regression for handling autocorrelation of network data in a transductive setting, file dd9e0c63-a044-1e9c-e053-3a05fe0a45ef
|
12
|
|
Segmentation-aided classification of hyperspectral data using spatial dependency of spectral bands, file dd9e0c65-787e-1e9c-e053-3a05fe0a45ef
|
9
|
|
Mining microscopic and macroscopic changes in network data streams, file dd9e0c69-cff7-1e9c-e053-3a05fe0a45ef
|
9
|
|
CloFAST: closed sequential pattern mining using sparse and vertical id-lists, file dd9e0c6a-1fcc-1e9c-e053-3a05fe0a45ef
|
9
|
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Dealing with temporal and spatial correlations to classify outliers in geophysical data streams, file dd9e0c63-1ad0-1e9c-e053-3a05fe0a45ef
|
7
|
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Multi-type clustering and classification from heterogeneous networks, file dd9e0c6a-2955-1e9c-e053-3a05fe0a45ef
|
7
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|
A Co-training Strategy for Multiple View Clustering in Process Mining, file dd9e0c6a-62e7-1e9c-e053-3a05fe0a45ef
|
7
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|
Novel Reconstruction Errors for Saliency Detection in Hyperspectral Images, file dd9e0c6b-490d-1e9c-e053-3a05fe0a45ef
|
7
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Guest Editors’ Introduction: special issue of selected papers from ECML PKDD 2011, file dd9e0c65-75c7-1e9c-e053-3a05fe0a45ef
|
6
|
|
A Multi-Stage Machine Learning Approach to Predict Dengue Incidence: A Case Study in Mexico, file dd9e0c66-c3f6-1e9c-e053-3a05fe0a45ef
|
6
|
|
null, file dd9e0c62-7bbf-1e9c-e053-3a05fe0a45ef
|
5
|
|
null, file dd9e0c69-f431-1e9c-e053-3a05fe0a45ef
|
5
|
|
Active learning via collective inference in network regression problems, file dd9e0c6a-4392-1e9c-e053-3a05fe0a45ef
|
5
|
|
null, file dd9e0c6b-0e9a-1e9c-e053-3a05fe0a45ef
|
5
|
|
Transductive hyperspectral image classification: toward integrating spectral and relational features via an iterative ensemble system, file dd9e0c6b-334e-1e9c-e053-3a05fe0a45ef
|
5
|
|
Network Reconstruction for the Identification of miRNA: mRNA Interaction Networks, file dd9e0c62-5bf8-1e9c-e053-3a05fe0a45ef
|
4
|
|
Discovering Novelty Patterns from the Ancient Christian Inscriptions of Rome, file dd9e0c65-13bc-1e9c-e053-3a05fe0a45ef
|
4
|
|
Multi-Channel Deep Feature Learning for Intrusion Detection, file dd9e0c66-61de-1e9c-e053-3a05fe0a45ef
|
4
|
|
null, file dd9e0c69-bc68-1e9c-e053-3a05fe0a45ef
|
4
|
|
null, file dd9e0c6a-d83a-1e9c-e053-3a05fe0a45ef
|
4
|
|
Summarizing numeric spatial data streams by trend cluster discovery, file dd9e0c62-5e43-1e9c-e053-3a05fe0a45ef
|
3
|
|
null, file dd9e0c62-7529-1e9c-e053-3a05fe0a45ef
|
3
|
|
Leveraging the power of local spatial autocorrelation in geophysical interpolative clustering, file dd9e0c63-5f74-1e9c-e053-3a05fe0a45ef
|
3
|
|
null, file dd9e0c63-9892-1e9c-e053-3a05fe0a45ef
|
3
|
|
Anomaly detection in aerospace product manufacturing: Initial remarks, file dd9e0c64-08be-1e9c-e053-3a05fe0a45ef
|
3
|
|
null, file dd9e0c66-9b00-1e9c-e053-3a05fe0a45ef
|
3
|
|
Empowering change vector analysis with autoencoding in bi-temporal hyperspectral images, file dd9e0c66-d645-1e9c-e053-3a05fe0a45ef
|
3
|
|
null, file dd9e0c67-3e3b-1e9c-e053-3a05fe0a45ef
|
3
|
|
ORANGE: Outcome-Oriented Predictive Process Monitoring Based on Image Encoding and CNNs, file dd9e0c68-ca6e-1e9c-e053-3a05fe0a45ef
|
3
|
|
JKarma: A Highly-Modular Framework for Pattern-Based Change Detection on Evolving Data, file dd9e0c69-b336-1e9c-e053-3a05fe0a45ef
|
3
|
|
Spatial autocorrelation and entropy for renewable energy forecasting, file dd9e0c6a-4b5a-1e9c-e053-3a05fe0a45ef
|
3
|
|
null, file dd9e0c6a-5827-1e9c-e053-3a05fe0a45ef
|
3
|
|
null, file dd9e0c6a-f15c-1e9c-e053-3a05fe0a45ef
|
3
|
|
null, file dd9e0c6a-fb27-1e9c-e053-3a05fe0a45ef
|
3
|
|
An OWL Ontology for Supporting Semantic Services in Big Data Platforms, file 7d5ea984-f6bf-4da6-80ce-0b8beaf0ea51
|
2
|
|
Mining emotion-aware sequential rules at user-level from micro-blogs, file 8d8575b7-688f-45f8-a03f-ecc20926e26c
|
2
|
|
Enhancing Regression Models with Spatio-temporal Indicator Additions, file dd9e0c62-4512-1e9c-e053-3a05fe0a45ef
|
2
|
|
Wind Power Forecasting Using Time Series Cluster Analysis, file dd9e0c62-5c14-1e9c-e053-3a05fe0a45ef
|
2
|
|
An Intelligent Technique for Forecasting Spatially Correlated Time Series, file dd9e0c63-9734-1e9c-e053-3a05fe0a45ef
|
2
|
|
Transductive Relational Classification in the Co-training Paradigm, file dd9e0c64-2790-1e9c-e053-3a05fe0a45ef
|
2
|
|
Continuously Mining Sliding Window Trend Clusters in a Sensor Network, file dd9e0c64-3129-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c67-035b-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c67-06d8-1e9c-e053-3a05fe0a45ef
|
2
|
|
Saliency Detection for Hyperspectral Images via Sparse-Non Negative-Matrix-Factorization and novel Distance Measures, file dd9e0c69-5613-1e9c-e053-3a05fe0a45ef
|
2
|
|
Integrating microRNA target predictions for the discovery of gene regulatory networks: a semi-supervised ensemble learning approach, file dd9e0c6a-0591-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c6a-d99e-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c6a-ec92-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c6b-343e-1e9c-e053-3a05fe0a45ef
|
2
|
|
Leveraging colour-based pseudo-labels to supervise saliency detection in hyperspectral image datasets, file dd9e0c6b-6312-1e9c-e053-3a05fe0a45ef
|
2
|
|
Anomaly Detection for Public Transport and Air Pollution Analysis, file 1b4023c0-4b63-4289-81a8-7ff2fb427485
|
1
|
|
null, file dd9e0c62-4de4-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c62-5170-1e9c-e053-3a05fe0a45ef
|
1
|
|
Network Regression in Collective Inference Setting, file dd9e0c62-60ff-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c62-63e4-1e9c-e053-3a05fe0a45ef
|
1
|
|
Geographic Knowledge Discovery in INGENS: an Inductive Database Perspective, file dd9e0c62-68b7-1e9c-e053-3a05fe0a45ef
|
1
|
|
Integrating Cluster Analysis to the ARIMA Model for Forecasting Geosensor Data, file dd9e0c62-6b86-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c62-6bb3-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c62-6cbc-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c62-6e02-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c62-704b-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c62-706a-1e9c-e053-3a05fe0a45ef
|
1
|
|
A Grid-based Multi-Relational Approach to Process Mining, file dd9e0c62-70f8-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c62-71ea-1e9c-e053-3a05fe0a45ef
|
1
|
|
Transductive learning for spatial regression with co-training, file dd9e0c62-748c-1e9c-e053-3a05fe0a45ef
|
1
|
|
Relational learning of disjunctive patterns in spatial networks, file dd9e0c62-7cb1-1e9c-e053-3a05fe0a45ef
|
1
|
|
The effects of pruning methods on the predictive accuracy of induced decision trees, file dd9e0c62-bada-1e9c-e053-3a05fe0a45ef
|
1
|
|
Process Mining to Forecast the Future of Running Cases, file dd9e0c63-9635-1e9c-e053-3a05fe0a45ef
|
1
|
|
Multi-Relational Model Tree Induction Tightly-Coupled with a Relational Database, file dd9e0c63-96a6-1e9c-e053-3a05fe0a45ef
|
1
|
|
Using trend clusters for spatiotemporal interpolation of missing data in a sensor network, file dd9e0c63-9816-1e9c-e053-3a05fe0a45ef
|
1
|
|
Dealing with spatial autocorrelation in gene flow modeling, file dd9e0c63-9922-1e9c-e053-3a05fe0a45ef
|
1
|
|
Dealing with Spatial Autocorrelation when Learning Predictive Clustering
Trees, file dd9e0c63-9fdd-1e9c-e053-3a05fe0a45ef
|
1
|
|
Data Mining Techniques in Sensor Networks:
Summarization, Interpolation and Surveillance, file dd9e0c64-2a72-1e9c-e053-3a05fe0a45ef
|
1
|
|
Trend Cluster Based Kriging Interpolation in Sensor Data Networks, file dd9e0c64-2b25-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c64-9fd2-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c65-2e19-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c65-8141-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c65-f08e-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c66-9f9d-1e9c-e053-3a05fe0a45ef
|
1
|
|
Dealing with Class Imbalance in Android Malware Detection by Cascading Clustering and Classification, file dd9e0c66-d02b-1e9c-e053-3a05fe0a45ef
|
1
|
|
Detecting salient regions in a bi-temporal hyperspectral scene by iterating clustering and classification, file dd9e0c68-26ff-1e9c-e053-3a05fe0a45ef
|
1
|
| Totale |
1.293 |