When modeling technical processes, the training data regularly come from test plans, to reduce the number of experiments and to save time and costs. On the other hand, this leads to unobserved combinations of the inpu...
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ISBN:
(纸本)3540405046
When modeling technical processes, the training data regularly come from test plans, to reduce the number of experiments and to save time and costs. On the other hand, this leads to unobserved combinations of the input variables. In this article it is shown, that these unobserved configurations might lead to un-trainable parameters. Afterwards a possible design criterion is introduced, which avoids this drawback. Our approach is tested to model a welding process. The results show, that hybrid Bayesian networks are able to deal with yet unobserved in- and output data.
Automatically authoring or acquiring cases in the case-based reasoning (CBR) systems is recognized as a bottleneck issue that can determine whether a CBR system will be successful or not. In order to reduce human effo...
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ISBN:
(纸本)3540405046
Automatically authoring or acquiring cases in the case-based reasoning (CBR) systems is recognized as a bottleneck issue that can determine whether a CBR system will be successful or not. In order to reduce human effort required for authoring the cases, we propose a framework for authoring the case from the unstructured, free-text, historic maintenance data by applying natural language processing technology. This paper provides an overview of the proposed framework, and outlines its implementation, an automated case creation system for the Integrated Diagnostic System. Some experimental results for testing the framework are also presented.
datamining in education is a developing interdisciplinary research field also known as educational datamining (EDM). The goal is to understand students' learning process and identify the way by which they can le...
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ISBN:
(纸本)9781728107882
datamining in education is a developing interdisciplinary research field also known as educational datamining (EDM). The goal is to understand students' learning process and identify the way by which they can learn to improve educational outcomes. learning using IT is one of the most widely used methods for education in modern days. Digital learning gives students an experience of individual learning at any time as well as anywhere, so students get more interest, flexibility at learning. Knowing the preferences of students learning will help the instructors to design better learning materials and teaching styles. We have surveyed on the students of undergraduate level and evaluated the students in three categories: good, average and excellent. We have used four classification models: Support Vector machine (SVM), Logistic Regression (LR), Decision Tree and Random Forest (RF) model to predict the performance of students on basis of the impact of IT and other study mediums based on their results. In this article, we have identified different parameters or features from five different learning sectors or fields which can give an impact on the student's learning process. So, we have processed in a way that will find out the datamining model which can give better accuracy of student's performance and also can find out which parameters or features among the five fields are playing a great role in their academic results. Moreover, we can apply these features by inspiring good or average students to improve their learning process.
This document presents the 3nd internationalconference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2024), a premier forum for presenting academic and industry papers on bi...
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Today's multi-billion-dollar online advertising industry is restlessly focused on maximizing return on investment (ROI) and good campaign strategies derive after gaining insight from user and advertisement data de...
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With the electricity market liberalization, the distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. A fa...
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ISBN:
(纸本)3540405046
With the electricity market liberalization, the distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity customers. A fair insight on the customers' behavior will permit the definition of specific contract aspects based on the different consumption patterns. In this paper, we propose a KDD project applied to electricity consumption data from a utility client's database. To form the different customers' classes, and find a set of representative consumption patterns, a comparative analysis of the performance of the K-means, Kohonen Self-Organized Maps (SOM) and a Two-Level approach is made. Each customer class will be represented by its load profile obtained with the algorithm with best performance in the data set used.
The construction industry is experiencing explosive growth in its capability to, generate and collect data. Advances in data storage technology have allowed the transformation of an enormous amount of data into comput...
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ISBN:
(纸本)1853129259
The construction industry is experiencing explosive growth in its capability to, generate and collect data. Advances in data storage technology have allowed the transformation of an enormous amount of data into computerized database systems. Nowadays, there are many efforts to convert the large amounts of data into useful patterns or trends. Knowledge Discovery in database (KDD) is a process that combines datamining (DM) techniques from machinelearning, patternrecognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from a large database. By applying KDD and DM to the analysis of construction project data, this paper presents the results of a research that discovers the knowledge through KDD process to better identify recurring construction problems.
This study investigates the effectiveness of probability forecasts output by standardmachinelearning techniques (Neural Network, C4.5, K-Nearest Neighbours, Naive Bayes, SVM and HMM) when tested on time series datas...
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ISBN:
(纸本)3540287574
This study investigates the effectiveness of probability forecasts output by standardmachinelearning techniques (Neural Network, C4.5, K-Nearest Neighbours, Naive Bayes, SVM and HMM) when tested on time series datasets from various problem domains, Raw data was converted into a pattern classification problem using a sliding window approach, and the respective target prediction was set as some discretised future value in the time series sequence. Experiments were conducted in the online learning setting to model the way in which time series data is presented. The performance of each learner's probability forecasts was assessed using ROC curves, square loss, classification accuracy and Empirical Reliability Curves (ERC) [1]. Our results demonstrate that effective probability forecasts can be generated on time series data and we discuss the practical implications of this.
Model trees are tree-based regression models that associate leaves with linear regression models. A new method for the stepwise induction of model trees (SMOTI) has been developed. Its main characteristic is the const...
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ISBN:
(纸本)3540405046
Model trees are tree-based regression models that associate leaves with linear regression models. A new method for the stepwise induction of model trees (SMOTI) has been developed. Its main characteristic is the construction of trees with two types of nodes: regression nodes, which perform only straight-line regression, and splitting nodes, which partition the feature space. In this way, internal regression nodes contribute to the definition of multiple linear models and have a "global" effect, while straight-line regressions at leaves have only "local" effects. In this paper the problem of simplifying model trees with both regression and splitting nodes is faced. In particular two methods, named Reduced Error Pruning (REP) and Reduced Error Grafting (REG), are proposed. They are characterized by the use of an independent pruning set. The effect of the simplification on model trees induced with SMOTI is empirically investigated. Results are in favour of simplified trees in most cases.
Hidden Markov Models (HMM) are nowadays the most successful modeling approach for speech recognition. However, standard HMM require the assumption that adjacent feature vectors are statistically independent and identi...
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ISBN:
(纸本)3540405046
Hidden Markov Models (HMM) are nowadays the most successful modeling approach for speech recognition. However, standard HMM require the assumption that adjacent feature vectors are statistically independent and identically distributed. These assumptions can be relaxed by introducing neural networks in the HMM frame work. These neural networks particularly the Multi-Layer Perceptrons (MLP) estimate the posterior probabilities used by the HMM. We started in the frame work of this work, to investigate smoothing techniques combining MLP probabilities with those from others estimators with better properties for small values (e.g., a single Gaussian) in the framework of the learning of our MLP. The main goal of this paper is to compare the performance of speech recognition of an isolated speech Arabic databases obtained with (1) discrete HMM, (2) hybrid HMM/MLP approaches using a MLP to estimate the HMM emission probabilities and (3) hybrid FCM/HMM/MLP approaches using the Fuzzy C-Means (FCM) algorithm to segment the acoustic vectors.
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