Search results for " Classification"
showing 10 items of 1043 documents
Reliability of physical functioning measures in ambulatory subjects with MS.
2005
Background and Purpose. One of the primary reasons for measuring outcomes during rehabilitation is to determine the effect of physiotherapy. Repeated measurement situations are susceptible to several sources of error, including inconsistencies caused by the subject, the procedure, the instrument and the examiner. Therefore, the reliability of the measures needs to be examined. Method. The present study used a repeated-measures design. Two studies were undertaken to examine the test–retest and inter-rater reliability for physical functioning measures. The interval between the measurements was one week. The sample consisted of 19 ambulatory subjects with mutliple sclerosis (MS) in the test–re…
Emotional Dimensions of User Experience ? A User Psychological Analysis
2014
User psychology is a human–technology interaction research approach that uses psychological concepts, theories, and findings to structure problems of human–technology interaction. As the notion of user experience has become central in human–technology interaction research and in product development, it is necessary to investigate the user psychology of user experience. This analysis of emotional human–technology interaction is based on the psychological theory of basic emotions. Three studies, two laboratory experiments, and one field study are used to investigate the basic emotions and the emotional mind involved in user experience. The first and second experiments study the measurement of…
Semi-supervised Hyperspectral Image Classification with Graphs
2006
This paper presents a semi-supervised graph-based method for the classification of hyperspectral images. The method is designed to exploit the spatial/contextual information in the im- ages through composite kernels. The proposed method produces smoother classifications with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. Good accuracy in high dimensional spaces and low number of labeled samples (ill-posed situations) are produced as compared to standard inductive support vector machines.
Including invariances in SVM remote sensing image classification
2012
This paper introduces a simple method to include invariances in support vector machine (SVM) for remote sensing image classification. We rely on the concept of virtual support vectors, by which the SVM is trained with both the selected support vectors and synthetic examples encoding the invariance of interest. The algorithm is very simple and effective, as demonstrated in two particularly interesting examples: invariance to the presence of shadows and to rotations in patchbased image segmentation. The improved accuracy (around +6% both in OA and Cohen's κ statistic), along with the simplicity of the approach encourage its use and extension to encode other invariances and other remote sensin…
Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (…
2021
Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is needed regarding pathology-independent classifiers and their interpretation. Hence, our aim is to develop a pathology-independent classifier that provides prediction probabilities and explanations of the classification decisions. Spinal posture data of healthy subjects and various pathologies (back pain, spinal fusion, osteoarthritis), as well as synthetic data, were used for modeling. A one-class support vector machine was used as a pathology-independent classifier. The outputs were transformed into a probability distribution according to Plat…
Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia
2020
Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential. The field of machine learning offers algorithms for both parameter selection and combination. In this study, several established machine learning approaches including a method for the selection of suitable signal parameters and classification algorithms are a…
Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity
2020
Protruding impact peak is one of the features of vertical ground reaction force (GRF) that is related to injury risk while running. The present research is dedicated to predicting GRF impact peak appearance by setting a binary classification problem. Kinematic data, namely a number of raw signals in the sagittal plane, collected by the Vicon motion capture system (Oxford Metrics Group, UK) were employed as predictors. Therefore, the input data for the predictive model are presented as a multi-channel time series. Deep learning techniques, namely five convolutional neural network (CNN) models were applied to the binary classification analysis, based on a Multi-Layer Perceptron (MLP) classifi…
Optimal gossip algorithm for distributed consensus SVM training in wireless sensor networks
2009
In this paper, we consider the distributed training of a SVM using measurements collected by the nodes of aWireless Sensor Network in order to achieve global consensus with the minimum possible inter-node communications for data exchange. We derive a novel mathematical characterization for the optimal selection of partial information that neighboring sensors should exchange in order to achieve consensus in the network. We provide a selection function which ranks the training vectors in order of importance in the learning process. The amount of information exchange can vary, based on an appropriately chosen threshold value of this selection function, providing a desired trade-off between cla…
Classifying Economics for the Common Good: Connecting Sustainable Development Goals to JEL Codes
2020
How does economics research help in solving societal challenges? This brief note sheds additional light on this question by providing ways to connect Journal of Economic Literature (JEL) codes and Sustainable Development Goals (SDGs) of the United Nations. These simple linkages illustrate that the themes of SDGs have corresponding JEL classification codes. As the mappings presented here are necessarily imperfect and incomplete, there is plenty of room for improvements. In an ideal world, there would be a JEL classification system for SDGs, a separate JEL code for each of the 17 SDGs.