Multi-attribute Classification of Text Documents as a Tool for Ranking and Categorization of Educational Innovation Projects

2014 ◽  
pp. 404-416
Author(s):  
Alexey An ◽  
Bakytkan Dauletbakov ◽  
Eugene Levner
Keyword(s):  
Educational Innovation ◽  
Text Documents ◽  
Innovation Projects ◽  
Attribute Classification

Varying Naïve Bayes Models With Applications to Classification of Chinese Text Documents

2014 ◽  
Vol 32 (3) ◽  
pp. 445-456 ◽  
Author(s):  
Guoyu Guan ◽  
Jianhua Guo ◽  
Hansheng Wang
Keyword(s):  
Chinese Text ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Text Documents

scholarly journals Classification and Measurement of the Firms’ Resources and Capabilities Applied to Eco-Innovation Projects from a Resource-Based View Perspective

2018 ◽  
Vol 10 (9) ◽  
pp. 3161 ◽  
Author(s):  
Pilar Portillo-Tarragona ◽  
Sabina Scarpellini ◽  
Jose Moneva ◽  
Jesus Valero-Gil ◽  
Alfonso Aranda-Usón
Keyword(s):  
Natural Resources ◽  
Conceptual Framework ◽  
Case Studies ◽  
Corporate Sustainability ◽  
Policy Makers ◽  
Resource Based View ◽  
Depth Analysis ◽  
Innovation Projects ◽  
Resources And Capabilities

Interest from academics, policy–makers and practitioners in eco-innovation has increased as it enables the optimization of the use of natural resources improving competitiveness and it provides a conceptual framework for corporate sustainability. In this context, this paper provides an in-depth analysis and a wide classification of the specific indicators for the integrated measurement of eco-innovation projects in business from a resource-based view (RBV). The specific metrics were tested to measure the economic-financial and environmental resources and capabilities applied by five Spanish firms to eco-innovation projects, selected as case studies.

A Generic Framework for Feature Representations in Image Categorization Tasks

2009 ◽  
Vol 1 (4) ◽  
pp. 36-60
Author(s):  
Adam Csapo ◽  
Barna Resko ◽  
Morten Lind ◽  
Peter Baranyi
Keyword(s):  
Visual Information ◽  
Research Field ◽  
Obstacle Detection ◽  
Point Of View ◽  
Visual Features ◽  
Text Documents ◽  
Feature Representations ◽  
The Past ◽  
Biological Perspective

The computerized modeling of cognitive visual information has been a research field of great interest in the past several decades. The research field is interesting not only from a biological perspective, but also from an engineering point of view when systems are developed that aim to achieve similar goals as biological cognitive systems. This article introduces a general framework for the extraction and systematic storage of low-level visual features. The applicability of the framework is investigated in both unstructured and highly structured environments. In a first experiment, a linear categorization algorithm originally developed for the classification of text documents is used to classify natural images taken from the Caltech 101 database. In a second experiment, the framework is used to provide an automatically guided vehicle with obstacle detection and auto-positioning functionalities in highly structured environments. Results demonstrate that the model is highly applicable in structured environments, and also shows promising results in certain cases when used in unstructured environments.

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