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Found 7 papers, 0 papers with code
Feature Relevance Analysis to Explain Concept Drift -- A Case Study in Human Activity Recognition
As a main result of this article, it is shown that feature relevance analysis cannot only be used to detect the concept drift but also to explain the reason for the drift when a limited number of typical reasons for the concept drift are predefined.
Hyperbolic Uncertainty Aware Semantic Segmentation
Semantic segmentation (SS) aims to classify each pixel into one of the pre-defined classes.
Better Classifier Calibration for Small Data Sets
When the amount of data for training is limited, the traditional approach to improve calibration starts to crumble.
Better Multi-class Probability Estimates for Small Data Sets
Many classification applications require accurate probability estimates in addition to good class separation but often classifiers are designed focusing only on the latter.
From User-independent to Personal Human Activity Recognition Models Exploiting the Sensors of a Smartphone
In this study, a novel method to obtain user-dependent human activity recognition models unobtrusively by exploiting the sensors of a smartphone is presented.
Personalizing human activity recognition models using incremental learning
At first, the recognition is based on user-independent model.
Importance of user inputs while using incremental learning to personalize human activity recognition models
Inertial sensor data from three body positions are used, and the classification is based on Learn++ ensemble method.
