Search results for "Upervised learning"

2017

OBJECTIVE Traditionally, gait analysis has been centered on the idea of average behavior and normality. On one hand, clinical diagnoses and therapeutic interventions typically assume that average gait patterns remain constant over time. On the other hand, it is well known that all our movements are accompanied by a certain amount of variability, which does not allow us to make two identical steps. The purpose of this study was to examine changes in the intra-individual gait patterns across different time-scales (i.e., tens-of-mins, tens-of-hours). METHODS Nine healthy subjects performed 15 gait trials at a self-selected speed on 6 sessions within one day (duration between two subsequent ses…

NeXt for neuro-radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique

2017

Stereotactic neuro-radiosurgery is a well-established therapy for intracranial diseases, especially brain metastases and highly invasive cancers that are difficult to treat with conventional surgery or radiotherapy. Nowadays, magnetic resonance imaging (MRI) is the most used modality in radiation therapy for soft-tissue anatomical districts, allowing for an accurate gross tumor volume (GTV) segmentation. Investigating also necrotic material within the whole tumor has significant clinical value in treatment planning and cancer progression assessment. These pathological necrotic regions are generally characterized by hypoxia, which is implicated in several aspects of tumor development and gro…

Unsupervised representation learning of spontaneous MEG data with nonlinear ICA

2023

Funding Information: We wish to thank the reviewers and editors for the useful comments to improve the paper a lot. We thank Dr. Hiroshi Morioka for the useful discussion at the beginning of the project. L.P. was funded in part by the European Research Council (No. 678578 ). A.H. was supported by a Fellowship from CIFAR, and the Academy of Finland. The authors acknowledge the computational resources provided by the Aalto Science-IT project, and also wish to thank the Finnish Grid and Cloud Infrastructure (FGCI) for supporting this project with computational and data storage resources. | openaire: EC/H2020/678578/EU//HRMEG Resting-state magnetoencephalography (MEG) data show complex but stru…

Application of selected supervised classification methods to bank marketing campaign

2016

Supervised classification covers a number of data mining methods based on training data. These methods have been successfully applied to solve multi-criteria complex classification problems in many domains, including economical issues. In this paper we discuss features of some supervised classification methods based on decision trees and apply them to the direct marketing campaigns data of a Portuguese banking institution. We discuss and compare the following classification methods: decision trees, bagging, boosting, and random forests. A classification problem in our approach is defined in a scenario where a bank’s clients make decisions about the activation of their deposits. The obtained…

Automated Detection of Microaneurysms Using Scale-Adapted Blob Analysis and Semi-Supervised Learning

2014

International audience; Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are then introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier to detect true MAs. The developed system is built using only…

Anomaly detection approach to keystroke dynamics based user authentication

2017

Keystroke dynamics is one of the authentication mechanisms which uses natural typing pattern of a user for identification. In this work, we introduced Dependence Clustering based approach to user authentication using keystroke dynamics. In addition, we applied a k-NN-based approach that demonstrated strong results. Most of the existing approaches use only genuine users data for training and validation. We designed a cross validation procedure with artificially generated impostor samples that improves the learning process yet allows fair comparison to previous works. We evaluated the methods using the CMU keystroke dynamics benchmark dataset. Both proposed approaches outperformed the previou…

Feature Extractors for Describing Vehicle Routing Problem Instances

2016

The vehicle routing problem comes in varied forms. In addition to usual variants with diverse constraints and specialized objectives, the problem instances themselves – even from a single shared source - can be distinctly different. Heuristic, metaheuristic, and hybrid algorithms that are typically used to solve these problems are sensitive to this variation and can exhibit erratic performance when applied on new, previously unseen instances. To mitigate this, and to improve their applicability, algorithm developers often choose to expose parameters that allow customization of the algorithm behavior. Unfortunately, finding a good set of values for these parameters can be a tedious task that…