Search results for "Intelligence"

Chapter 11. Computational representation of FrameNet for multilingual natural language generation

2021

Erratum to: A New Feature Selection Methodology for K-mers Representation of DNA Sequences

2017

SVG rendering for internet imaging

2006

The SVG (scalable vector graphics) standard allows representing complex graphical scenes by a collection of graphic vectorial-based primitives, offering several advantages with respect to classical raster images such as: scalability, resolution independence, etc. In this paper we present a full comparison between some advanced raster to SVG algorithms: SWaterG, SVGenie, SVGWave and some commercial tools. SWaterG works by a watershed decomposition coupled with some ad-hoc heuristics, SVGenie and SVGWave use a polygonalization based respectively on data dependent and wavelet triangulation. The results obtained by SWaterG, SVGenie and SVGWave are satisfactory both in terms of perceptual measur…

Collaborative Activities and Methods

2016

Having described the context for collaborative activities (in Chap. 1), and reviewed the basic aspects of computer supported decision-making activities (in Chap. 2), we will present in this section several specific methods used in collaborative decision making. The methods and techniques presented in the chapter are independent of the information technologies upon they are instantiated.

Mining Interpretable Rules for Sentiment and Semantic Relation Analysis Using Tsetlin Machines

2020

Tsetlin Machines (TMs) are an interpretable pattern recognition approach that captures patterns with high discriminative power from data. Patterns are represented as conjunctive clauses in propositional logic, produced using bandit-learning in the form of Tsetlin Automata. In this work, we propose a TM-based approach to two common Natural Language Processing (NLP) tasks, viz. Sentiment Analysis and Semantic Relation Categorization. By performing frequent itemset mining on the patterns produced, we show that they follow existing expert-verified rule-sets or lexicons. Further, our comparison with other widely used machine learning techniques indicates that the TM approach helps maintain inter…

Clustering categorical data: A stability analysis framework

2011

Clustering to identify inherent structure is an important first step in data exploration. The k-means algorithm is a popular choice, but K-means is not generally appropriate for categorical data. A specific extension of k-means for categorical data is the k-modes algorithm. Both of these partition clustering methods are sensitive to the initialization of prototypes, which creates the difficulty of selecting the best solution for a given problem. In addition, selecting the number of clusters can be an issue. Further, the k-modes method is especially prone to instability when presented with ‘noisy’ data, since the calculation of the mode lacks the smoothing effect inherent in the calculation …

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...

ERP qualification exploiting waveform, spectral and time-frequency infomax

2008

The present contribution briefly introduces an event related potential (ERP) detector. The specified detector includes three kinds of features of ERP. They are the ERP waveform feature, ERP spectral feature and ERP time-frequency feature respectively. According to these characteristics, two parameters are defined to reflect the timing feature of ERP. The mismatch negativity (MMN) is taken as the example to design an exact qualification detector. The experiment validates that the computer can automatically detect the raw trace to reflect the quality of the dataset, qualify the filtered trace to test whether the artifacts have been filtered out, and select the ERP-like component to reject art…

Analyse des Visuellen Klassifikationssystems Durch Detektionsexperimente

1977

Summary Experiments on recognizing statistically distorted patterns show that the human visual system operates as a linear classifier. The spatial frequency range, within which features are extracted, is determined by the coupling in the area of sharpest vision (2°). The relevant features for classifying patterns are not produced by isotropic filtering

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.