Search results for "koneoppiminen"
showing 10 items of 218 documents
Koneoppiminen
2015
Koneoppiminen on monipuolinen ja tehokas työkalu erilaisiin tehtäviin. Tässä tutkielmassa on tarkoituksena tutustua sekä ohjatun että ohjaamattoman oppimisen yleisimpiin menetelmiin. Tarkoituksena on käsitellä nämä menetelmät yleisellä tasolla niin, että asiasta tietämätön ymmärtää perusperiaatteet miten eri menetelmät toimivat. Tarkempia yksityiskohtia ja matemaattisia algoritmejä ei tulla käsittelemään. Machine learning is powerfull and versatile tool for a multitude of tasks. The point of this study is to familiarize oneself with a couple of different methods of both supervised and unsupervised machine learning, so that anyone can understand the basic principles behind the different meth…
Unstable feature relevance in classification tasks
2011
Knowledge discovery using diffusion maps
2013
Aberrant brain functional networks in type 2 diabetes mellitus: A graph theoretical and support-vector machine approach
2022
ObjectiveType 2 diabetes mellitus (T2DM) is a high risk of cognitive decline and dementia, but the underlying mechanisms are not yet clearly understood. This study aimed to explore the functional connectivity (FC) and topological properties among whole brain networks and correlations with impaired cognition and distinguish T2DM from healthy controls (HC) to identify potential biomarkers for cognition abnormalities.MethodsA total of 80 T2DM and 55 well-matched HC were recruited in this study. Subjects’ clinical data, neuropsychological tests and resting-state functional magnetic resonance imaging data were acquired. Whole-brain network FC were mapped, the topological characteristics were ana…
Sparse minimal learning machine using a diversity measure minimization
2019
The minimal learning machine (MLM) training procedure consists in solving a linear system with multiple measurement vectors (MMV) created between the geometric congurations of points in the input and output spaces. Such geometric congurations are built upon two matrices created using subsets of input and output points, named reference points (RPs). The present paper considers an extension of the focal underdetermined system solver (FOCUSS) for MMV linear systems problems with additive noise, named regularized MMV FOCUSS (regularized M-FOCUSS), and evaluates it in the task of selecting input reference points for regression settings. Experiments were carried out using UCI datasets, where the …
Model selection for Extreme Minimal Learning Machine using sampling
2019
A combination of Extreme Learning Machine (ELM) and Minimal Learning Machine (MLM)—to use a distance-based basis from MLM in the ridge regression like learning framework of ELM—was proposed in [8]. In the further experiments with the technique [9], it was concluded that in multilabel classification one can obtain a good validation error level without overlearning simply by using the whole training data for constructing the basis. Here, we consider possibilities to reduce the complexity of the resulting machine learning model, referred as the Extreme Minimal Leaning Machine (EMLM), by using a bidirectional sampling strategy: To sample both the feature space and the space of observations in o…
On Assessing Vulnerabilities of the 5G Networks to Adversarial Examples
2022
The use of artificial intelligence and machine learning is recognized as the key enabler for 5G mobile networks which would allow service providers to tackle the network complexity and ensure security, reliability and allocation of the necessary resources to their customers in a dynamic, robust and trustworthy way. Dependability of the future generation networks on accurate and timely performance of its artificial intelligence components means that disturbance in the functionality of these components may have negative impact on the entire network. As a result, there is an increasing concern about the vulnerability of intelligent machine learning driven frameworks to adversarial effects. In …
Tekoälyn perusteita ja sovelluksia
2019
Koneoppimisen hyödyntäminen konenäössä
2016
Konenäön hyödyntäminen yleistyy ja sitä mukaa myös konenäön ongelmat monimutkaistuvat. Yksi suosittu tapa ratkaista näitä ongelmia on hyödyntää koneoppimista. Tässä tutkielmassa tarkastellaan miten koneoppimista hyödynnetään konenäössä ja vertaillaan eri koneoppimisalgoritmeja konenäön näkökulmasta. omputer Vision faces increasing challenges as its used more. Common way to solve these complex problems is to use Machine Learning. In this thesis workings of different Machine Learing algorithms are looked on and their advantages and disadvantages are compared.