Search results for "Machine learning"
showing 10 items of 1464 documents
Machine learning in management accounting research: Literature review and pathways for the future
2021
This paper explores the possibilities of machine learning (ML) methods in management accounting research and showcases one future avenue in practice by applying ML-based textual literature review to ML/AI research in accounting. The review reveals that machine learning methods in management accounting (MA) are still in their infancy, and current research in accounting has progressed in and focused mainly on three areas related to ML and AI: 1) effects on the field of accounting and the development of the accounting profession, 2) textual analysis related to accounting data/reports, and 3) prediction methods. Based on our literature review and recently published related ML research from othe…
Dataset shift adaptation with active queries
2011
In remote sensing image classification, it is commonly assumed that the distribution of the classes is stable over the entire image. This way, training pixels labeled by photointerpretation are assumed to be representative of the whole image. However, differences in distribution of the classes throughout the image make this assumption weak and a model built on a single area may be suboptimal when applied to the rest of the image. In this paper, we investigate the use of active learning to correct the shifts that may appear when training and test data do not come from the same distribution. Experiments are carried out on a VHR remote sensing classification scenario showing that active learni…
Reverse engineering expert visual observations: From fixations to the learning of spatial filters with a neural-gas algorithm
2013
Human beings can become experts in performing specific vision tasks, for example, doctors analysing medical images, or botanists studying leaves. With sufficient knowledge and experience, people can become very efficient at such tasks. When attempting to perform these tasks with a machine vision system, it would be highly beneficial to be able to replicate the process which the expert undergoes. Advances in eye-tracking technology can provide data to allow us to discover the manner in which an expert studies an image. This paper presents a first step towards utilizing these data for computer vision purposes. A growing-neural-gas algorithm is used to learn a set of Gabor filters which give h…
An integrated framework for risk profiling of breast cancer patients following surgery.
2006
Objective: An integrated decision support framework is proposed for clinical oncologists making prognostic assessments of patients with operable breast cancer. The framework may be delivered over a web interface. It comprises a triangulation of prognostic modelling, visualisation of historical patient data and an explanatory facility to interpret risk group assignments using empirically derived Boolean rules expressed directly in clinical terms. Methods and materials: The prognostic inferences in the interface are validated in a multicentre longitudinal cohort study by modelling retrospective data from 917 patients recruited at Christie Hospital, Wilmslow between 1983 and 1989 and predictin…
A probabilistic approach to learning a visually grounded language model through human-robot interaction
2010
A Language is among the most fascinating and complex cognitive activities that develops rapidly since the early months of infants' life. The aim of the present work is to provide a humanoid robot with cognitive, perceptual and motor skills fundamental for the acquisition of a rudimentary form of language. We present a novel probabilistic model, inspired by the findings in cognitive sciences, able to associate spoken words with their perceptually grounded meanings. The main focus is set on acquiring the meaning of various perceptual categories (e. g. red, blue, circle, above, etc.), rather than specific world entities (e. g. an apple, a toy, etc.). Our probabilistic model is based on a varia…
IMAGE PROCESSING, SEGMENTATION AND MACHINE LEARNING MODELS TO CLASSIFY AND DELINEATE TUMOR VOLUMES TO SUPPORT MEDICAL DECISION
2020
Techniques for processing and analysing images and medical data have become the main’s translational applications and researches in clinical and pre-clinical environments. The advantages of these techniques are the improvement of diagnosis accuracy and the assessment of treatment response by means of quantitative biomarkers in an efficient way. In the era of the personalized medicine, an early and efficacy prediction of therapy response in patients is still a critical issue. In radiation therapy planning, Magnetic Resonance Imaging (MRI) provides high quality detailed images and excellent soft-tissue contrast, while Computerized Tomography (CT) images provides attenuation maps and very good…
Novel scaffold of natural compound eliciting sweet taste revealed by machine learning
2020
Abstract Sugar replacement is still an active issue in the food industry. The use of structure-taste relationships remains one of the most rational strategy to expand the chemical space associated to sweet taste. A new machine learning model has been setup based on an update of the SweetenersDB and on open-source molecular features. It has been implemented on a freely accessible webserver. Cellular functional assays show that the sweet taste receptor is activated in vitro by a new scaffold of natural compounds identified by the in silico protocol. The newly identified sweetener belongs to the lignan chemical family and opens a new chemical space to explore.
A Learning Automata Based Solution to Service Selection in Stochastic Environments
2010
Published version of a paper published in the book: Trends in Applied Intelligent Systems. Also available on SpringerLink: http://dx.doi.org/10.1007/978-3-642-13033-5_22 With the abundance of services available in today’s world, identifying those of high quality is becoming increasingly difficult. Reputation systems can offer generic recommendations by aggregating user provided opinions about service quality, however, are prone to ballot stuffing and badmouthing . In general, unfair ratings may degrade the trustworthiness of reputation systems, and changes in service quality over time render previous ratings unreliable. In this paper, we provide a novel solution to the above problems based …
On Obtaining Classification Confidence, Ranked Predictions and AUC with Tsetlin Machines
2020
Tsetlin machines (TMs) are a promising approach to machine learning that uses Tsetlin Automata to produce patterns in propositional logic, leading to binary (hard) classifications. In many applications, however, one needs to know the confidence of classifications, e.g. to facilitate risk management. In this paper, we propose a novel scheme for measuring TM confidence based on the logistic function, calculated from the propositional logic patterns that match the input. We then use this scheme to trade off precision against recall, producing area under receiver operating characteristic curves (AUC) for TMs. Empirically, using four real-world datasets, we show that AUC is a more sensitive meas…
Modelling Dependencies Between Classifiers in Mobile Masquerader Detection
2004
The unauthorised use of mobile terminals may result in an abuse of sensitive information kept locally on the terminals or accessible over the network. Therefore, there is a need for security means capable of detecting the cases when the legitimate user of the terminal is substituted. The problem of user substitution detection is considered in the paper as a problem of classifying the behaviour of the person interacting with the terminal as originating from the user or someone else. Different aspects of behaviour are analysed by designated one-class classifiers whose classifications are subsequently combined. A modification of majority voting that takes into account some of the dependencies …