Search results for "pattern"
showing 10 items of 4203 documents
Bioinformatics and Computational Biology
2009
Bioinformatics is a new, rapidly expanding field that uses computational approaches to answer biological questions (Baxevanis, 2005). These questions are answered by means of analyzing and mining biological data. The field of bioinformatics or computational biology is a multidisciplinary research and development environment, in which a variety of techniques from computer science, applied mathematics, linguistics, physics, and, statistics are used. The terms bioinformatics and computational biology are often used interchangeably (Baldi, 1998; Pevzner, 2000). This new area of research is driven by the wealth of data from high throughput genome projects, such as the human genome sequencing pro…
Improving the k-NCN classification rule through heuristic modifications
1998
Abstract This paper presents an empirical investigation of the recently proposed k-Nearest Centroid Neighbours ( k -NCN) classification rule along with two heuristic modifications of it. These alternatives make use of both proximity and geometrical distribution of the prototypes in the training set in order to estimate the class label of a given sample. The experimental results show that both alternatives give significantly better classification rates than the k -Nearest Neighbours rule, basically due to the properties of the plain k -NCN technique.
A new shape-oriented classification method for UV/VIS-spectra
1996
A new shape-oriented classification method is described. It is shown, how shapes of UV/VIS-spectra can be classified and coded and how a classification technique can be used to improve database search operations for pre-selections or even shape-oriented identifications.
Semi-supervised classification using tree-based self-organizing maps
2011
Published version of an article from the following onference prodeedings: AI 2011: Advances in Artificial Intelligence. Also available from the publisher on SpringerLink: http://dx.doi.org/10.1007/978-3-642-25832-9_3 This paper presents a classifier which uses a tree-based Neural Network (NN), and uses both, unlabeled and labeled instances. First, we learn the structure of the data distribution in an unsupervised manner. After convergence, and once labeled data become available, our strategy tags each of the clusters according to the evidence provided by the instances. Unlike other neighborhood-based schemes, our classifier uses only a small set of representatives whose cardinality can be m…
A Simple Cluster Validation Index with Maximal Coverage
2017
Clustering is an unsupervised technique to detect general, distinct profiles from a given dataset. Similarly to the existence of various different clustering methods and algorithms, there exists many cluster validation methods and indices to suggest the number of clusters. The purpose of this paper is, firstly, to propose a new, simple internal cluster validation index. The index has a maximal coverage: also one cluster, i.e., lack of division of a dataset into disjoint subsets, can be detected. Secondly, the proposed index is compared to the available indices from five different packages implemented in R or Matlab to assess its utilizability. The comparison also suggests many interesting f…
Building a Maturity Model for Developing Ethically Aligned AI Systems
2021
Ethical concerns related to Artificial Intelligence (AI) equipped systems are prompting demands for ethical AI from all directions. As a response, in recent years public bodies, governments, and companies have rushed to provide guidelines and principles for how AI-based systems are designed and used ethically. We have learned, however, that high-level principles and ethical guidelines cannot be easily converted into actionable advice for industrial organizations that develop AI-based information systems. Maturity models are commonly used in software and systems development companies as a roadmap for improving the performance. We argue that they could also be applied in the context of develo…
A Robust Minimal Learning Machine based on the M-Estimator
2017
In this paper we propose a robust Minimal Learning Machine (R-RLM) for regression problems. The proposed method uses a robust M-estimator to generate a linear mapping between input and output distances matrices of MLM. The R-MLM was tested on one synthetic and three real world datasets that were contaminated with an increasing number of outliers. The method achieved a performance comparable to the robust Extreme Learning Machine (R-RLM) and thus can be seen as a valid alternative for regression tasks on datasets with outliers. peerReviewed
Supplementary data for Ün et. al. 2020 "Cytoplasmic incompatibility between New and Old World populations of a tramp ant"
2020
Supplementary annotation and phylogenetic data. See included README file for details.
Concept Drift Detection Using Online Histogram-Based Bayesian Classifiers
2016
In this paper, we present a novel algorithm that performs online histogram-based classification, i.e., specifically designed for the case when the data is dynamic and its distribution is non-stationary. Our method, called the Online Histogram-based Naïve Bayes Classifier (OHNBC) involves a statistical classifier based on the well-established Bayesian theory, but which makes some assumptions with respect to the independence of the attributes. Moreover, this classifier generates a prediction model using uni-dimensional histograms, whose segments or buckets are fixed in terms of their cardinalities but dynamic in terms of their widths. Additionally, our algorithm invokes the principles of info…
Conceptual representations of actions for autonomous robots
2001
An autonomous robot involved in long and complex missions should be able to generate, update and process its own plans of action. In this perspective, it is not plausible that the meaning of the representations used by the robot is given from outside the system itself. Rather, the meaning of internal symbols must be firmly anchored to the world through the perceptual abilities and the overall activities of the robot. According to these premises, in this paper we present an approach to action representation that is based on a "conceptual" level of representation, acting as an intermediate level between symbols and data coming from sensors. Symbolic representations are interpreted by mapping …