Search results for "machine learning."
showing 10 items of 1455 documents
PerPot – a meta-model and software tool for analysis and optimisation of load-performance-interaction
2004
The Performance Potential meta-model PerPot simulates the interaction between load and performance in adaptive physiological processes like training in sport by means of antagonistic dynamics.The t...
A Sub-Symbolic Approach to Word Modelling for Domain Specific Speech Recognition
2006
In this work a sub-symbolic technique for automatic, data driven language models construction is presented. Such a technique can be used to arrange a language-modelling module, which can be easily integrated in existing speech recognition architectures, such as the well-found HTK architecture. The proposed technique takes advantages from both the traditional LSA approach and from a novel application of a probability space metric known as "Hellinger's distance". Experimental trials are also presented, in order to validate the proposed approach.
Structural Classification of Complex Molecules by Artificial Intelligence Techniques
2011
Algorithms for classification and taxonomy bases on criteria, e.g., information entropy. The feasibility of replacing a given molecule by similar ones in the composition of a complex drug is studied. Some local anaesthetics currently in use are classified using structural properties. In taxonomy the detailed comparison of the sequences of biomolecules, proteins or nucleic acids, allows the reconstruction of a molecular phylogenetic tree. The method is applied to the classifications of (1) indazolols (against Trichomonas vaginalis), (2) fullerenes and fullerite, (3) living and heat-inactivated lactic acid bacteria against cytokines, (4) phylogenesis of avian birds and 1918 influenza virus, (…
HUMAN BEHAVIOR IN A MULTI-CRITERIA CHOICE PROBLEM WITH INDIVIDUAL TASKS OF DIFFERENT DIFFICULTIES
2003
This paper is devoted to a laboratory study of human behavior in a multi-criteria choice problem. The specific feature of the experimental study is the creation of an individually adjusted instance of a general task for each subject in accordance with his/her preferences over each criterion. Human behavior is studied in a specially constructed choice situation based on the decomposition of the alternatives of a multi-criteria problem. The procedure is based on multiple steps of pair-wise comparisons involving only some (two or three) of the original components of the alternatives. Abilities of subjects to use such comparisons and to answer the questions in a logical way are tested. The exp…
MedAI: Transparency in Medical Image Segmentation
2021
MedAI: Transparency in Medical Image Segmentation is a challenge held for the first time at the Nordic AI Meet that focuses on medical image segmentation and transparency in machine learning (ML)-based systems. We propose three tasks to meet specific gastrointestinal image segmentation challenges collected from experts within the field, including two separate segmentation scenarios and one scenario on transparent ML systems. The latter emphasizes the need for explainable and interpretable ML algorithms. We provide a development dataset for the participants to train their ML models, tested on a concealed test dataset.
Advances in the statistical methodology for the selection of image descriptors for visual pattern representation and classification
1995
Recent advances in the statistical methodology for selecting optimal subsets of features (image descriptors) for visual pattern representation and classification are presented. The paper attempts to provide a guideline about which approach to choose with respect to the a priori knowledge of the problem. Two basic approaches are reviewed and the conditions under which they should be used are specified. References to more detailed material about each one of the methods are given and experimental results supporting the main conclusions are briefly outlined.
<title>Dynamic integration of multiple data mining techniques in a knowledge discovery management system</title>
1999
One of the most important directions in improvement of data mining and knowledge discovery, is the integration of multiple classification techniques of an ensemble of classifiers. An integration technique should be able to estimate and select the most appropriate component classifiers from the ensemble. We present two variations of an advanced dynamic integration technique with two distance metrics. The technique is one variation of the stacked generalization method, with an assumption that each of the component classifiers is the best one, inside a certain sub area of the entire domain area. Our technique includes two phases: the learning phase and the application phase. During the learnin…
Machine Learning Techniques for Automatic Depression Assessment
2018
Depression is one of the most common mood disorder that is inherently related to emotions, involving bad mood, low self-esteem and loss of interest in normal pleasurable activities. The aim of this work is to develop a framework based on the dataset provided by AVEC'14 for depression assessment. The proposed work presents two different motion representation methods: a) Gabor Motion History Image (GMHI), and b) Motion History Image (MHI). Several combinations of appearance-based low level features are extracted from both motion representations. These features were further combined with statistically derived features, and used for training and testing with several machine learning techniques.…
Machine Learning for Modeling the Biomechanical Behavior of Human Soft Tissue
2016
An accurate modeling of the biomechanical properties of human soft tissue is crucial in many clinical applications, such as, radiotherapy administration or surgery. The finite element method (FEM) is the usual choice to carry out such modeling due to its high accuracy. However, FEM is computationally very costly, and hence, its application in real-time or even off-line with short delays are still challenges to overcome. This paper proposes a framework based on Machine Learning to learn FEM modeling, thus having a tool able to yield results that may be sufficiently fast for clinical applications. In particular, the use of ensembles of Decision Trees has shown its suitability in modeling the …
Listwise Recommendation Approach with Non-negative Matrix Factorization
2018
Matrix factorization (MF) is one of the most effective categories of recommendation algorithms, which makes predictions based on the user-item rating matrix. Nowadays many studies reveal that the ultimate goal of recommendations is to predict correct rankings of these unrated items. However, most of the pioneering efforts on ranking-oriented MF predict users’ item ranking based on the original rating matrix, which fails to explicitly present users’ preference ranking on items and thus might result in some accuracy loss. In this paper, we formulate a novel listwise user-ranking probability prediction problem for recommendations, that aims to utilize a user-ranking probability matrix to predi…