| Nome |
# |
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A novel spectral-spatial co-training algorithm for the transductive classification of hyperspectral imagery data, file dd9e0c6b-30a4-1e9c-e053-3a05fe0a45ef
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69
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Using multiple time series analysis for geosensor data forecasting, file dd9e0c6a-e1db-1e9c-e053-3a05fe0a45ef
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63
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Active learning via collective inference in network regression problems, file 34d3b78a-88ac-4c76-9c8d-13af9d1622b8
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54
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Segmentation-aided classification of hyperspectral data using spatial dependency of spectral bands, file dd9e0c6b-2368-1e9c-e053-3a05fe0a45ef
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48
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Collective regression for handling autocorrelation of network data in a transductive setting, file dd9e0c6c-0d22-1e9c-e053-3a05fe0a45ef
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38
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Advanced Programming of Intelligent Social Robots, file dd9e0c67-3165-1e9c-e053-3a05fe0a45ef
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37
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null, file dd9e0c6a-a939-1e9c-e053-3a05fe0a45ef
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36
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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
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18
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Using multiple time series analysis for geosensor data forecasting, file dd9e0c64-059b-1e9c-e053-3a05fe0a45ef
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17
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Collective regression for handling autocorrelation of network data in a transductive setting, file dd9e0c63-a044-1e9c-e053-3a05fe0a45ef
|
12
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Segmentation-aided classification of hyperspectral data using spatial dependency of spectral bands, file dd9e0c65-787e-1e9c-e053-3a05fe0a45ef
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9
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null, file dd9e0c63-9913-1e9c-e053-3a05fe0a45ef
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8
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Dealing with temporal and spatial correlations to classify outliers in geophysical data streams, file dd9e0c63-1ad0-1e9c-e053-3a05fe0a45ef
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7
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A Co-training Strategy for Multiple View Clustering in Process Mining, file dd9e0c6a-62e7-1e9c-e053-3a05fe0a45ef
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7
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Novel Reconstruction Errors for Saliency Detection in Hyperspectral Images, file dd9e0c6b-490d-1e9c-e053-3a05fe0a45ef
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7
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A Multi-Stage Machine Learning Approach to Predict Dengue Incidence: A Case Study in Mexico, file dd9e0c66-c3f6-1e9c-e053-3a05fe0a45ef
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6
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null, file dd9e0c62-7bbf-1e9c-e053-3a05fe0a45ef
|
5
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Active learning via collective inference in network regression problems, file dd9e0c6a-4392-1e9c-e053-3a05fe0a45ef
|
5
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null, file dd9e0c6b-0e9a-1e9c-e053-3a05fe0a45ef
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5
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Transductive hyperspectral image classification: toward integrating spectral and relational features via an iterative ensemble system, file dd9e0c6b-334e-1e9c-e053-3a05fe0a45ef
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5
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Exploiting spatial correlation of spectral signature for training data selection in hyperspectral image classification, file dd9e0c64-08c0-1e9c-e053-3a05fe0a45ef
|
4
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Multi-Channel Deep Feature Learning for Intrusion Detection, file dd9e0c66-61de-1e9c-e053-3a05fe0a45ef
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4
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null, file dd9e0c6a-d83a-1e9c-e053-3a05fe0a45ef
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4
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Summarizing numeric spatial data streams by trend cluster discovery, file dd9e0c62-5e43-1e9c-e053-3a05fe0a45ef
|
3
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Leveraging the power of local spatial autocorrelation in geophysical interpolative clustering, file dd9e0c63-5f74-1e9c-e053-3a05fe0a45ef
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3
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null, file dd9e0c63-9892-1e9c-e053-3a05fe0a45ef
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3
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Anomaly detection in aerospace product manufacturing: Initial remarks, file dd9e0c64-08be-1e9c-e053-3a05fe0a45ef
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3
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null, file dd9e0c66-9b00-1e9c-e053-3a05fe0a45ef
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3
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Empowering change vector analysis with autoencoding in bi-temporal hyperspectral images, file dd9e0c66-d645-1e9c-e053-3a05fe0a45ef
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3
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null, file dd9e0c67-3e3b-1e9c-e053-3a05fe0a45ef
|
3
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ORANGE: Outcome-Oriented Predictive Process Monitoring Based on Image Encoding and CNNs, file dd9e0c68-ca6e-1e9c-e053-3a05fe0a45ef
|
3
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null, file dd9e0c6a-5827-1e9c-e053-3a05fe0a45ef
|
3
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null, file dd9e0c6a-f15c-1e9c-e053-3a05fe0a45ef
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3
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null, file dd9e0c6a-fb27-1e9c-e053-3a05fe0a45ef
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3
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Enhancing Regression Models with Spatio-temporal Indicator Additions, file dd9e0c62-4512-1e9c-e053-3a05fe0a45ef
|
2
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Wind Power Forecasting Using Time Series Cluster Analysis, file dd9e0c62-5c14-1e9c-e053-3a05fe0a45ef
|
2
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An Intelligent Technique for Forecasting Spatially Correlated Time Series, file dd9e0c63-9734-1e9c-e053-3a05fe0a45ef
|
2
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Transductive Relational Classification in the Co-training Paradigm, file dd9e0c64-2790-1e9c-e053-3a05fe0a45ef
|
2
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Continuously Mining Sliding Window Trend Clusters in a Sensor Network, file dd9e0c64-3129-1e9c-e053-3a05fe0a45ef
|
2
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null, file dd9e0c67-035b-1e9c-e053-3a05fe0a45ef
|
2
|
|
null, file dd9e0c67-06d8-1e9c-e053-3a05fe0a45ef
|
2
|
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Saliency Detection for Hyperspectral Images via Sparse-Non Negative-Matrix-Factorization and novel Distance Measures, file dd9e0c69-5613-1e9c-e053-3a05fe0a45ef
|
2
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|
null, file dd9e0c6a-8d9b-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
|
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Leveraging colour-based pseudo-labels to supervise saliency detection in hyperspectral image datasets, file dd9e0c6b-6312-1e9c-e053-3a05fe0a45ef
|
2
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Network Regression with Predictive Clustering Trees, file dd9e0c62-49a5-1e9c-e053-3a05fe0a45ef
|
1
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MINING COMPLEX PATTERNS, file dd9e0c62-60d0-1e9c-e053-3a05fe0a45ef
|
1
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Network Regression in Collective Inference Setting, file dd9e0c62-60ff-1e9c-e053-3a05fe0a45ef
|
1
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null, file dd9e0c62-646f-1e9c-e053-3a05fe0a45ef
|
1
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Geographic Knowledge Discovery in INGENS: an Inductive Database Perspective, file dd9e0c62-68b7-1e9c-e053-3a05fe0a45ef
|
1
|
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Integrating Cluster Analysis to the ARIMA Model for Forecasting Geosensor Data, file dd9e0c62-6b86-1e9c-e053-3a05fe0a45ef
|
1
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A Grid-based Multi-Relational Approach to Process Mining, file dd9e0c62-70f8-1e9c-e053-3a05fe0a45ef
|
1
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Transductive learning for spatial regression with co-training, file dd9e0c62-748c-1e9c-e053-3a05fe0a45ef
|
1
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Process Mining to Forecast the Future of Running Cases, file dd9e0c63-9635-1e9c-e053-3a05fe0a45ef
|
1
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Multi-Relational Model Tree Induction Tightly-Coupled with a Relational Database, file dd9e0c63-96a6-1e9c-e053-3a05fe0a45ef
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1
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Using trend clusters for spatiotemporal interpolation of missing data in a sensor network, file dd9e0c63-9816-1e9c-e053-3a05fe0a45ef
|
1
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Recent advances in mining patterns from complex data, file dd9e0c63-9894-1e9c-e053-3a05fe0a45ef
|
1
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Dealing with spatial autocorrelation in gene flow modeling, file dd9e0c63-9922-1e9c-e053-3a05fe0a45ef
|
1
|
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Dealing with Spatial Autocorrelation when Learning Predictive Clustering
Trees, file dd9e0c63-9fdd-1e9c-e053-3a05fe0a45ef
|
1
|
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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
|
|
Dealing with Class Imbalance in Android Malware Detection by Cascading Clustering and Classification, file dd9e0c66-d02b-1e9c-e053-3a05fe0a45ef
|
1
|
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Detecting salient regions in a bi-temporal hyperspectral scene by iterating clustering and classification, file dd9e0c68-26ff-1e9c-e053-3a05fe0a45ef
|
1
|
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Clustering-Aided Multi-View Classification: A Case Study on Android Malware Detection, file dd9e0c68-5ef9-1e9c-e053-3a05fe0a45ef
|
1
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Leveraging shallow machine learning to predict business process behavior, file dd9e0c69-360c-1e9c-e053-3a05fe0a45ef
|
1
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Activity Prediction of Business Process Instances with Inception CNN Models, file dd9e0c69-3b78-1e9c-e053-3a05fe0a45ef
|
1
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null, file dd9e0c69-b4e1-1e9c-e053-3a05fe0a45ef
|
1
|
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null, file dd9e0c69-e4b0-1e9c-e053-3a05fe0a45ef
|
1
|
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null, file dd9e0c69-f6e1-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c6a-5080-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c6a-5081-1e9c-e053-3a05fe0a45ef
|
1
|
|
null, file dd9e0c6b-1137-1e9c-e053-3a05fe0a45ef
|
1
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Leveraging Grad-CAM to Improve the Accuracy of Network Intrusion Detection Systems, file dd9e0c6b-973c-1e9c-e053-3a05fe0a45ef
|
1
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GAN augmentation to deal with imbalance in imaging-based intrusion detection, file dd9e0c6c-09e7-1e9c-e053-3a05fe0a45ef
|
1
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ROULETTE: A neural attention multi-output model for explainable Network Intrusion Detection, file dd9e0c6c-5b72-1e9c-e053-3a05fe0a45ef
|
1
|
| Totale |
557 |