| Nome |
# |
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Calibration techniques for binary classification problems: A comparative analysis, file e163de42-ec8a-19c7-e053-6605fe0a8397
|
316
|
|
Efficient approaches for solving the large-scale k-medoids problem, file e163de42-ec89-19c7-e053-6605fe0a8397
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193
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Relaxed Dissimilarity-based Symbolic Histogram Variants for Granular Graph Embedding, file e163de42-f03b-19c7-e053-6605fe0a8397
|
93
|
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(Hyper)Graph embedding and classification via simplicial complexes, file e163de42-ec94-19c7-e053-6605fe0a8397
|
85
|
|
Complexity vs. performance in granular embedding spaces for graph classification, file e163de42-ec26-19c7-e053-6605fe0a8397
|
73
|
|
Stochastic information granules extraction for graph embedding and classification, file e163de42-ec8f-19c7-e053-6605fe0a8397
|
71
|
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Intrusion detection in wi-fi networks by modular and optimized ensemble of classifiers, file e163de42-ec84-19c7-e053-6605fe0a8397
|
70
|
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ANFIS synthesis by clustering for microgrids EMS design, file e163de42-ec92-19c7-e053-6605fe0a8397
|
50
|
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Predicting lorawan behavior. How machine learning can help, file e163de42-eced-19c7-e053-6605fe0a8397
|
44
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(Hyper)graph kernels over simplicial complexes, file e163de42-ecf3-19c7-e053-6605fe0a8397
|
42
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Exploratory approach for network behavior clustering in LoRaWAN, file e163de42-ecef-19c7-e053-6605fe0a8397
|
36
|
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An infoveillance system for detecting and tracking relevant topics from italian tweets during the COVID-19 event, file e163de42-ec93-19c7-e053-6605fe0a8397
|
32
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|
Modelling and recognition of protein contact networks by multiple kernel learning and dissimilarity representations, file e163de42-eceb-19c7-e053-6605fe0a8397
|
32
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Editorial: Prediction and explanation in biomedicine using network-based approaches, file 54542a00-eb3e-4063-901d-459e45dc0bc8
|
24
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|
On component-wise dissimilarity measures and metric properties in pattern recognition, file 2b417374-415e-4592-83d5-218f925b139a
|
23
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Multifractal Characterization and Modeling of Blood Pressure Signals, file 52839896-d567-4881-950c-9ec3221c74de
|
21
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Brain metabolic differences between pure bulbar and pure spinal ALS: a 2-[18F]FDG-PET study, file 62349ea9-62f7-4c08-bcd2-e563f2b2a516
|
20
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On Information Granulation via Data Clustering for Granular Computing-Based Pattern Recognition: A Graph Embedding Case Study, file e163de42-fb1c-19c7-e053-6605fe0a8397
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20
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Role of brain 2-[18F]fluoro-2-deoxy-D-glucose-positron-emission tomography as survival predictor in amyotrophic lateral sclerosis, file 11b0d0b0-f487-4699-bfee-596f2f53ca4d
|
19
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Calibration techniques for binary classification problems: A comparative analysis, file e163de42-ec8b-19c7-e053-6605fe0a8397
|
18
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Stochastic information granules extraction for graph embedding and classification, file e163de42-ec91-19c7-e053-6605fe0a8397
|
17
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Exploratory approach for network behavior clustering in LoRaWAN, file f9d7ff33-2178-460e-b82f-40509fe79a2d
|
16
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Intrusion detection in wi-fi networks by modular and optimized ensemble of classifiers, file e163de42-ec86-19c7-e053-6605fe0a8397
|
15
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On Information Granulation via Data Filtering for Granular Computing-Based Pattern Recognition: A Graph Embedding Case Study, file 7a1f03a2-b29b-414d-a901-0fd61d2785a8
|
12
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Complexity vs. performance in granular embedding spaces for graph classification, file e163de42-ec28-19c7-e053-6605fe0a8397
|
11
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An Unsupervised Graph-Based Approach for Detecting Relevant Topics: A Case Study on the Italian Twitter Cohort during the Russia–Ukraine Conflict, file 8f464201-6fdf-49d5-9741-672e68a47f91
|
10
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Stochastic information granules extraction for graph embedding and classification, file e163de42-ec90-19c7-e053-6605fe0a8397
|
10
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Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis, file 784f4ebb-3c58-4610-8938-551b4c0ad6b0
|
8
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Calibration techniques for binary classification problems: A comparative analysis, file e163de42-ec8c-19c7-e053-6605fe0a8397
|
8
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The universal phenotype, file e163de42-ecf2-19c7-e053-6605fe0a8397
|
6
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Human versus Machine Intelligence: Assessing Natural Language Generation Models through Complex Systems Theory, file b5927e51-5e21-4478-9fa5-e24c755295d3
|
5
|
|
A Comparison of Neural Word Embedding Language Models for Classifying Social Media Users in the Healthcare Context, file 3ebc35c8-61ef-4af4-a7ce-0e27e7751724
|
3
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A Multi-objective Optimization Approach for the Synthesis of Granular Computing-Based Classification Systems in the Graph Domain, file dcc45ca2-69bb-41b2-a212-bd41379fa495
|
3
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Supervised approaches for function prediction of proteins contact networks from topological structure information, file e163de42-e8e1-19c7-e053-6605fe0a8397
|
3
|
|
An ecology-based index for text embedding and classification, file e163de42-ec2b-19c7-e053-6605fe0a8397
|
3
|
|
Multi-attribute group decision making based on T-spherical fuzzy soft rough average aggregation operators, file e163de42-f437-19c7-e053-6605fe0a8397
|
3
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Presynaptic Dopaminergic Imaging Characterizes Patients with REM Sleep Behavior Disorder Due to Synucleinopathy, file 21711d8a-3ca0-467f-9239-001c7af8f374
|
2
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A statistical framework for labeling unlabelled data: a case study on anomaly detection in pressurization systems for high-speed railway trains, file 4f35d8c1-4e49-4db2-b773-0f030ef7b114
|
2
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Sex-related differences in Amyotrophic Lateral Sclerosis: a brain 2-[18F]FDG-PET study, file 88f0c5a3-bdff-41b4-9e7a-cf0652cb71c5
|
2
|
|
A Granular Computing Approach for Multi-Labelled Sequences Classification in IEEE 802.11 Networks, file 9ffa052b-00b8-48e8-86e6-0263894db18c
|
2
|
|
Metodi (Multi)Kernel nel Dominio dei Grafi, file e163de42-e8e0-19c7-e053-6605fe0a8397
|
2
|
|
An ecology-based index for text embedding and classification, file e163de42-e8e2-19c7-e053-6605fe0a8397
|
2
|
|
Spazi di Embedding per la Classificazione di Grafi, file e163de42-ec2a-19c7-e053-6605fe0a8397
|
2
|
|
On the optimization of embedding spaces via information granulation for pattern recognition, file e163de42-ec7f-19c7-e053-6605fe0a8397
|
2
|
|
On the optimization of embedding spaces via information granulation for pattern recognition, file e163de42-ec82-19c7-e053-6605fe0a8397
|
2
|
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Dynamic Ensemble Inference at the Edge, file e163de42-ef5d-19c7-e053-6605fe0a8397
|
2
|
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Predicting phenoconversion of REM sleep behaviour disorder due to synucleinopathy using dopaminergic imaging, file 4fedb1b7-bc77-4176-8072-ecb30803e017
|
1
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|
A class-specific metric learning approach for graph embedding by information granulation, file e163de42-e9a1-19c7-e053-6605fe0a8397
|
1
|
|
Pattern Recognition in spazi non-metrici: a biological case study, file e163de42-ec79-19c7-e053-6605fe0a8397
|
1
|
|
Towards a Class-Aware Information Granulation for Graph Embedding and Classification, file e163de42-ec7c-19c7-e053-6605fe0a8397
|
1
|
|
Exploiting cliques for granular computing-based graph classification, file e163de42-ec7d-19c7-e053-6605fe0a8397
|
1
|
|
Data mining by evolving agents for clusters discovery and metric learning, file e163de42-ec80-19c7-e053-6605fe0a8397
|
1
|
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Efficient approaches for solving the large-scale k-medoids problem: towards structured data, file e163de42-ec83-19c7-e053-6605fe0a8397
|
1
|
|
A novel algorithm for online inexact string matching and its FPGA implementation, file e163de42-ece9-19c7-e053-6605fe0a8397
|
1
|
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An enhanced filtering-based information granulation procedure for graph embedding and classification, file e163de42-ecec-19c7-e053-6605fe0a8397
|
1
|
|
A generalized framework for ANFIS synthesis procedures by clustering techniques, file e163de42-ecf1-19c7-e053-6605fe0a8397
|
1
|
|
Intrusion Detection in Wi-Fi Networks by Modular and Optimized Ensemble of Classifiers: An Extended Analysis, file e163de42-fae3-19c7-e053-6605fe0a8397
|
1
|
| Totale |
1.446 |