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The Power of Ensembles for Active Learning in Image Classification
TLDR
It is found that ensembles perform better and lead to more calibrated predictive uncertainties, which are the basis for many active learning algorithms, and Monte-Carlo Dropout uncertainties perform worse. Expand
A Brief Survey of Text Mining
TLDR
The main analysis tasks preprocessing, classification, clustering, information extraction and visualization are described and a number of successful applications of text mining are discussed. Expand
CHAOS Challenge - Combined (CT-MR) Healthy Abdominal Organ Segmentation
TLDR
The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance, but the best MSSD performance remains limited, and multi-tasking DL models designed to segment all organs are observed to perform worse compared to organ-specific ones. Expand
A Comparative Study on Language Identification Methods
TLDR
This work presents the evaluation results and discusses the importance of a dynamic value for the out-of-place measure and the Ad-Hoc Ranking classification method. Expand
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
TLDR
Five different deep learning models and their Ensemble have been used, to classify COVID-19, pneumoniae and healthy subjects using Chest X-Ray, and qualitative results depicted the ResNets to be the most interpretable model. Expand
An Experimental Comparison of Similarity Adaptation Approaches
TLDR
This work describes ways to address the problem of constraint violations and compares the different approaches for modeling and learning individual distance measures as a weighted linear combination of multiple facets in different application scenarios. Expand
Fuzzy Control - Fundamentals, Stability and Design of Fuzzy Controllers
TLDR
Details of the design of Fuzzy Controllers and their parametrization and Optimization are provided for the first time in a unified model. Expand
Creating a Cluster Hierarchy under Constraints of a Partially Known Hierarchy
TLDR
This paper analyzes a scenario, where constraints are derived from a hierarchy that is partially known in advance, and introduces the concept of hierarchical constraints and continues by presenting and evaluating two approaches using them. Expand
A comparative analysis of offline and online evaluations and discussion of research paper recommender system evaluation
TLDR
It is found that results of offline and online evaluations often contradict each other, and it is concluded that offline evaluations may be inappropriate for evaluating research paper recommender systems, in many settings. Expand
Research paper recommender system evaluation: a quantitative literature survey
TLDR
It is currently not possible to determine which recommendation approaches for academic literature recommendation are the most promising, but there is little value in the existence of more than 80 approaches if the best performing approaches are unknown. Expand
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