Mathematical models are normally used to calculate the component concentrations in biological wastewater treatment. However, this work deals with the wastewater from a coke plant and it implies inhibition effects betw...
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ISBN:
(纸本)3540379517
Mathematical models are normally used to calculate the component concentrations in biological wastewater treatment. However, this work deals with the wastewater from a coke plant and it implies inhibition effects between components which do not permit the use of said mathematical models. Due to this, feed-forwardneuralnetworks were used to estimate the ammonium concentration in the effluent stream of the biological plant. The architecture of the neural network is based on previous works in this topic. The methodology consists in performing a group of different sizes of the hidden layer and different subsets of input variables.
Hierarchical neuralnetworks show many benefits when employed for classification problems even when only simple methods analogous,to decision trees are used to retrieve the classification result. More complex ways of ...
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ISBN:
(纸本)3540379517
Hierarchical neuralnetworks show many benefits when employed for classification problems even when only simple methods analogous,to decision trees are used to retrieve the classification result. More complex ways of evaluating the hierarchy output that take into account the complete information the hierarchy provides yield improved classification results. Due to the hierarchical output space decomposition that is inherent to hierarchical neuralnetworks the usage of Dempster-Shafer evidence theory suggests itself as it allows for the representation of evidence at different levels of abstraction. Moreover, it provides the possibility to differentiate between uncertainty and ignorance. The proposed approach. has been evaluated using three different data sets and showed consistently improved classification results compared to the simple decision-tree-like retrieval method.
The two key factors to design an ensemble of neuralnetworks are how to train the individual networks and how to combine the different outputs to get a single output. In this paper we focus on the combination module. ...
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ISBN:
(纸本)3540379517
The two key factors to design an ensemble of neuralnetworks are how to train the individual networks and how to combine the different outputs to get a single output. In this paper we focus on the combination module. We have proposed two methods based on Stacked Generalization as the combination module of an ensemble of neuralnetworks. In this paper we have performed a comparison among the two versions of Stacked Generalization and six statistical combination methods in order to get the best combination method. We have used the mean increase of performance and the mean percentage or error reduction for the comparison. The results show that the methods based on Stacked Generalization are better than classical combiners.
We explore the use of constraints with divisive hierarchical clustering. We mention some considerations on the effects of the inclusion of constraints into the hierarchical clustering process. Furthermore, we introduc...
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ISBN:
(纸本)3540379517
We explore the use of constraints with divisive hierarchical clustering. We mention some considerations on the effects of the inclusion of constraints into the hierarchical clustering process. Furthermore, we introduce an implementation of a semi-supervised divisive hierarchical clustering algorithm and show the influence of including constraints into the divisive hierarchical clustering process. In this task our main interest lies in building stable dendrograms when clustering with different subsets of data.
Recently, two extensions of neural gas have been proposed: a fast batch version of neural gas for data given in advance, and extensions of neural gas to learn a (possibly fuzzy) supervised classification. Here we prop...
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ISBN:
(纸本)3540379517
Recently, two extensions of neural gas have been proposed: a fast batch version of neural gas for data given in advance, and extensions of neural gas to learn a (possibly fuzzy) supervised classification. Here we propose a batch version for supervised neural gas training which allows to efficiently learn a prototype-based classification, provided training data are given beforehand. The method relies on a simpler cost function than online supervised neural gas and leads to simpler update formulas. We prove convergence of the algorithm in a general framework, which also incorporates supervised k-means and supervised batch-SOM, and which opens the way towards metric adaptation as well as application to proximity data not embedded in a real-vector space.
We extend the self-organizing map (SOM) in the form as proposed by Heskes to a supervised fuzzy classification method. On the one hand, this leads to a robust classifier where efficient learning with fuzzy labeled or ...
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ISBN:
(纸本)3540379517
We extend the self-organizing map (SOM) in the form as proposed by Heskes to a supervised fuzzy classification method. On the one hand, this leads to a robust classifier where efficient learning with fuzzy labeled or partially contradictory data is possible. On the other hand, the integration of labeling into the location of prototypes in a SOM leads to a visualization of those parts of the data relevant for the classification.
A new convolutional neural network model termed sparse convolutional neural network (SCNN) is presented and its usefulness for real-time object detection in gray-valued, monocular video sequences is demonstrated. SCNN...
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ISBN:
(纸本)3540379517
A new convolutional neural network model termed sparse convolutional neural network (SCNN) is presented and its usefulness for real-time object detection in gray-valued, monocular video sequences is demonstrated. SCNNs are trained on "raw" gray values and are intended to perform feature selection as a part of regular neural network training. For this purpose, the learning rule is extended by an unsupervised component which performs a local nonlinear principal components analysis: in this way, meaningful and diverse properties can be computed from local image patches. The SCNN model can be used to train classifiers for different object classes which share a common first layer, i.e., a common preprocessing. This is of advantage since the information needs only to be calculated once for all classifiers. It is further demonstrated how SCNNs can be implemented by successive convolutions of the input image: scanning an image for objects at all possible locations is shown to be possible in real-time using this technique.
A novel approach to feature selection from unlabeled vector data is presented. It is based on the reconstruction of original data relationships in an auxiliary space with either weighted or omitted features. Feature w...
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ISBN:
(纸本)3540379517
A novel approach to feature selection from unlabeled vector data is presented. It is based on the reconstruction of original data relationships in an auxiliary space with either weighted or omitted features. Feature weighting, on one hand;is related to the return forces of factors in a parametric data similarity measure as response to disturbance of their optimum values. Feature omission, on the other hand, inducing measurable loss of reconstruction quality, is realized in an iterative greedy way. The proposed framework allows to apply custom data similarity measures. Here, adaptive Euclidean distance and adaptive Pearson correlation are considered, the former serving as standard reference, the latter being, usefully for intensity data. Results of the different strategies are given for chromatography and gene expression data.
Microarray technologies are increasingly being used in biological and medical sciences for high throughput analyses of genetic information on the genome, transcriptome and proteome levels. The differentiation between ...
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ISBN:
(纸本)3540379517
Microarray technologies are increasingly being used in biological and medical sciences for high throughput analyses of genetic information on the genome, transcriptome and proteome levels. The differentiation between cancerous and benign processes in the body often poses a difficult diagnostic problem in the clinical setting while being of major importance for the treatment of patients. In this situation, feature reduction techniques capable of reducing the dimensionality of data are essential for building predictive tools based on classification. We extend the set covering machine of Marchand and Shawe-Taylor to data dependent rays in order to achieve a feature reduction and direct interpretation of the found conjunctions of intervals on individual genes. We give bounds for the generalization error as a function of the amount of data compression and the number of training errors achieved during training. In experiments with artificial data and a real world data set of gene expression profiles from the pancreas we show the utility of the approach and its applicability to microarray data classification.
Supervised learning models most commonly use crisp labels for classifier training. Crisp labels fail to capture the data characteristics when overlapping classes exist. In this work we attempt to compare between learn...
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ISBN:
(纸本)3540379517
Supervised learning models most commonly use crisp labels for classifier training. Crisp labels fail to capture the data characteristics when overlapping classes exist. In this work we attempt to compare between learning using soft and hard labels to train K-nearest neighbor classifiers. We propose a new technique to generate soft labels based on fuzzy-clustering of the data and fuzzy relabelling of cluster prototypes. Experiments were conducted on five data sets to compare between classifiers that learn using different types of soft labels and classifiers that learn with crisp labels. Results reveal that learning with soft labels is more robust against label errors opposed to learning with crisp labels. The proposed technique to find soft labels from the data, was also found to lead to a more robust training in most data sets investigated.
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