Advanced Approaches to Intelligent Information and Database by Janusz Sobecki, Veera Boonjing, Suphamit Chittayasothorn

By Janusz Sobecki, Veera Boonjing, Suphamit Chittayasothorn

This e-book contains 35 chapters offering diverse theoretical and functional facets of clever info and Database platforms. these days either clever and Database platforms are utilized in many of the components of human actions which necessitates extra study in those components. during this ebook a number of attention-grabbing concerns regarding the clever info versions and techniques in addition to their complex purposes, database platforms purposes, info versions and their research and electronic multimedia tools and functions are provided and mentioned either from the sensible and theoretical issues of view. The ebook is equipped in 4 elements dedicated to clever platforms versions and techniques, clever structures complicated functions, database platforms tools and functions and multimedia structures equipment and purposes. The booklet might be attention-grabbing for practitioners and researchers, specifically graduate and PhD scholars of knowledge know-how and desktop technology, to boot more matured lecturers and experts drawn to constructing and verification of clever details, database and multimedia structures versions, tools and functions. The readers of this quantity are enabled to discover many inspiring rules and motivating useful examples that would support them within the present and destiny work.

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Comparison done on real free open-source ERP data shows that random forest of oblique decision trees is very efficient. Keywords: ERP configuration, free text classification, random forest of oblique decision trees, ERP5. 1 Introduction Enterprise resource planning (ERP) systems or enterprise systems are software systems for business management, encompassing modules supporting most of (if not all) functional areas in companies [1]: planning, manufacturing, sales, marketing, distribution, accounting, financial, human resource management.

Do, S. Moga, and P. Lenca decision are then aggregated to classify a new individual. A popular aggregation function is a (unweighted) majority vote of the trees. Classical random forests (RF) uses a single variable for node splitting. 5-OCE with the Off-Centred-Entropy OCE [28]. e. when dependencies among variable are more likely to occur, multivariate splitting can be preferred. We thus also used random forests of oblique decision trees (RF-ODT) that perform multivariate node splitting with linear SVMs [33].

For that purpose it maximizes the distance (or margin) between the supporting planes for each class. Thus a point falling on the wrong side of its supporting plane is considered to be an error. Therefore, a SVM algorithm tries to simultaneously maximize the margin and minimize the error. g. linear, polynomial, radial basis or a sigmoid function, and a cost constant to tune the errors and margin size). 2 Random Forest Random forests, introduced in [30], are an ensemble learning method that aim to create a collection of decision trees (the forest).

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