Search results for "machine"

<title>Expanding context against weighted voting of classifiers</title>

2000

In the paper we propose a new method to integrate the predictions of multiple classifiers for Data Mining and Machine Learning tasks. The method assumes that each classifier stands in it's own context, and the contexts are partially ordered. The order is defined by monotonous quality function that maps each context to the value from the interval [0,1]. The classifier that has the context with better quality is supposed to predict better than the classifier from worse quality. The objective is to generate the opinion of `virtual' classifier that stands in the context with quality equal to 1. This virtual classifier must have the best accuracy of predictions due to the best context. To do thi…

2020

Abstract. Despite the availability of both commercial and open-source software, an ideal tool for digital rock physics analysis for accurate automatic image analysis at ambient computational performance is difficult to pinpoint. More often, image segmentation is driven manually, where the performance remains limited to two phases. Discrepancies due to artefacts cause inaccuracies in image analysis. To overcome these problems, we have developed CobWeb 1.0, which is automated and explicitly tailored for accurate greyscale (multiphase) image segmentation using unsupervised and supervised machine learning techniques. In this study, we demonstrate image segmentation using unsupervised machine le…

Acquisition of Higher Order Knowledge by a Dynamic Modeling Environment Based on the Educational Concept of Self-Regulated Learning

2013

I aim to show that learning with this modeling based Educational Learning System (ELS) can accomplish the target of achieving higher order knowledge. The ELS is a system consisting of internal and external elements. The external prerequisites consist of technical and physical elements and the internal ones are shaped by the students pre-knowledge and the instructors teaching competencies including his/her social, emotional, and disciplinary knowledge necessary for teaching. The ELS is based on a theoretical framework of different theories and models such as concept mapping, elaboration of mental models, cognitive tool-approach, and self-regulated learning (SRL). Different features for visua…

Diversity in search strategies for ensemble feature selection

2005

Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. It was shown theoretically and experimentally that in order for an ensemble to be effective, it should consist of base classifiers that have diversity in their predictions. One technique, which proved to be effective for constructing an ensemble of diverse base classifiers, is the use of different feature subsets, or so-called ensemble feature selection. Many ensemble feature selection strategies incorporate diversity as an objective in the search for the best collection of feature subse…

<title>Distance functions in dynamic integration of data mining techniques</title>

2000

One of the most important directions in the improvement of data mining and knowledge discovery is the integration of multiple data mining techniques. An integration method needs to be able either to evaluate and select the most appropriate data mining technique or to combine two or more techniques efficiently. A recent integration method for the dynamic integration of multiple data mining techniques is based on the assumption that each of the data mining techniques is the best one inside a certain subarea of the whole domain area. This method uses an instance-based learning approach to collect information about the competence areas of the mining techniques and applies a distance function to…

Machine Learning Methods for Spatial and Temporal Parameter Estimation

2020

Monitoring vegetation with satellite remote sensing is of paramount relevance to understand the status and health of our planet. Accurate and constant monitoring of the biosphere has large societal, economical, and environmental implications, given the increasing demand of biofuels and food by the world population. The current democratization of machine learning, big data, and high processing capabilities allow us to take such endeavor in a decisive manner. This chapter proposes three novel machine learning approaches to exploit spatial, temporal, multi-sensor, and large-scale data characteristics. We show (1) the application of multi-output Gaussian processes for gap-filling time series of…

Computerunterstützte Diagnostik in der Thoraxradiologie - aktuelle Schwerpunkte und Techniken

2003

The proliferation of digital data sets and the increasing amount of images, e. g. through the use of multislice spiral CT or multiple follow-up examinations in the context of new therapies, are ideal prerequisites for computer-aided diagnosis (CAD) in chest radiology. Multiple studies have described the applications and advantages of computer assistance in performing different diagnostic tasks. More powerful computers will enable the introduction of these systems into the clinical routine and could provide an enormous increase in morphological and functional information. The commercial introduction of tools for detection and visualization of pulmonary nodules has already begun. This is one …

A generalizability measure for program synthesis with genetic programming

2021

The generalizability of programs synthesized by genetic programming (GP) to unseen test cases is one of the main challenges of GP-based program synthesis. Recent work showed that increasing the amount of training data improves the generalizability of the programs synthesized by GP. However, generating training data is usually an expensive task as the output value for every training case must be calculated manually by the user. Therefore, this work suggests an approximation of the expected generalization ability of solution candidates found by GP. To obtain candidate solutions that all solve the training cases, but are structurally different, a GP run is not stopped after the first solution …

Inference of Spatiotemporal Processes over Graphs via Kernel Kriged Kalman Filtering

2018

Inference of space-time signals evolving over graphs emerges naturally in a number of network science related applications. A frequently encountered challenge pertains to reconstructing such dynamic processes given their values over a subset of vertices and time instants. The present paper develops a graph-aware kernel-based kriged Kalman filtering approach that leverages the spatio-temporal dynamics to allow for efficient online reconstruction, while also coping with dynamically evolving network topologies. Laplacian kernels are employed to perform kriging over the graph when spatial second-order statistics are unknown, as is often the case. Numerical tests with synthetic and real data ill…

A word prediction methodology for automatic sentence completion

2015

Word prediction generally relies on n-grams occurrence statistics, which may have huge data storage requirements and does not take into account the general meaning of the text. We propose an alternative methodology, based on Latent Semantic Analysis, to address these issues. An asymmetric Word-Word frequency matrix is employed to achieve higher scalability with large training datasets than the classic Word-Document approach. We propose a function for scoring candidate terms for the missing word in a sentence. We show how this function approximates the probability of occurrence of a given candidate word. Experimental results show that the proposed approach outperforms non neural network lang…