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1. A classification of the "full" words (shi2zi4) (1) of the vernacular Chinese of the III-V centuries

Creator:
Gurevich, Isabella
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
classification and China
Language:
English
Description:
The aim of this paper is to rank the words of the Chinese language of the III-V centuries in a number of classes that differ in their grammatical characteristics. The classification undertaken is based on syntactic criteria.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

3. A learning algorithm for a novel neural network architecture motivated by integrate-and-fire neuron model

Creator:
Mishra, Deepak, Yadav, Abhishek, and Kalra, Prem K.
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Integrate-And-Fire Neuron Model, function approximation, classification, artificial neural network, and steepest descent method
Language:
English
Description:
In this paper, a learning algorithm for a novel neural network architecture motivated by Integrate-and-Fire Neuron Model (IFN) is proposed and tested for various applications where a multilayer perceptron (MLP) neural network is conventionally used. It is observed that inclusion of a few more biological phenomenon in the formulation of artificial neural networks make them more prevailing. Several benchmark and real-life problems of classification and function-approximation are illustrated.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

4. A new classification algorithm: optimally generalized learning vector quantization (OGLVQ)

Creator:
Temel, T.
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
machine learning, classification, learning vector quantization, self-organized mapping, supervised learning, and unsupervised learning
Language:
English
Description:
We present a new Generalized Learning Vector Quantization classifier called Optimally Generalized Learning Vector Quantization based on a novel weight-update rule for learning labeled samples. The algorithm attains stable prototype/weight vector dynamics in terms of estimated current and previous weights and their updates. Resulting weight update term is then related to the proximity measure used by Generalized Learning Vector Quantization classifiers. New algorithm and some major counterparts are tested and compared for synthetic and publicly available datasets. For both the datasets studied, it is seen that the new classifier outperforms its counterparts in training and testing with accuracy above 80% its counterparts and in robustness against model parameter varition.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

5. Alertness detection of system operator

Creator:
Tatarinov, Vladimír
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Discrete Fourier Transform, neural networks, parametric methods, electroencephalogram (EEG), micro-sleep detection, attention decrease, and classification
Language:
English
Description:
Attention decrease and an eventual micro-sleep of an artificial system operator is very dangerous and its early detection can prevent great losses. This work deals with an early detection of micro-sleep based on analysis of an electroencefalographic activity of tlie brain. There are classic spectral methods - the Discrete Fourier Transform and parametric methods - autoregressive models used for signal processing here. An influence of a band pass filter characteristic on classification is investigated. For the detection of the micro-sleep multi-layer perceptron, radial basis function (RBF) and the learning vector quantization (LVQ) neural networks are used. The k-nearest neighbor as a representative of non-parametric methods is examined. The last method used here is based on the Bayesian theory and its coefficients are found using the maximum likelihood estimation.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

6. An efficient model selection for SVM in real-world datasets using BGA and RGA

Creator:
Almasi , Omid Naghash, Akhtarshenas, Ehsan, and Rouhani, Modjtaba
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Model selection, model complexity, support vector machines, genetic algorithms, classification, and real-world datasets
Language:
English
Description:
Support vector machine (SVM) has become one of the most popular machine-learning methods during the last years. The design of an efficient model and the proper adjustment of the SVMs parameters are integral to reducing the testing time and enhancing performance. In this paper, a new bipartite objective function consisted of the sparseness property and generalization performance is proposed. Since the proposed objective function is based on selecting fewer numbers of the support vectors, the model complexity is reduced while the performance accuracy remains at an acceptable level. Due to the model complexity reduction, the testing time is decreased and the ability of SVM in practical applications is increased Moreover, to prove the performance of the proposed objective function, a comparative study was carried out on the proposed objective function and the conventional objective function, which is only based on the generalization performance, using the Binary Genetic Algorithm (BGA) and Real-valued vectors GA (RGA). The effectiveness of the proposed cost function is demonstrated based on the results of the comparative study on four real-world datasets of UCI database.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

7. An improved classifier and transliterator of hand-written Palmyrene letters to Latin

Creator:
Hamplová, Adéla, Franc, David, and Veselý, Arnošt
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
artificial intelligence, classification, historical alphabets, mobilenet, and computer vision
Language:
English
Description:
This article presents the problem of improving the classifier of handwritten letters from historical alphabets, using letter classification algorithms and transliterating them to Latin. We apply it on Palmyrene alphabet, which is a complex alphabet with letters, some of which are very similar to each other. We created a mobile application for Palmyrene alphabet that is able to transliterate hand-written letters or letters that are given as photograph images. At first, the core of the application was based on MobileNet, but the classification results were not suitable enough. In this article, we suggest an improved, better performing convolutional neural network architecture for hand-written letter classifier used in our mobile application. Our suggested new convolutional neural network architecture shows an improvement in accuracy from 0.6893 to 0.9821 by 142% for hand-written model in comparison with the original MobileNet. Future plans are to improve the photographic model as well.
Rights:
http://creativecommons.org/licenses/by-nc-sa/4.0/ and policy:public

8. An improved odor recognition system using learning vector quantization with a new discriminant analysis

Creator:
Temel, Turgay and Karlik, Bekir
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Olfactory system, classification, odor recognition, pre-processing, and learning vector quantization
Language:
English
Description:
A new pre-processing algorithm for improved discrimination of odor samples is proposed. The pre-processed odor sample outputs from two sensors are input using a learning-vector quantization (LVQ) classifier as a means of odor recognition to be employed within electronic nose applications. The proposed algorithm brings out highly scattered classes while minimizing the within-class scatter of the samples given an odor class. LVQ is observed to operate robustly and reliably in terms of variation of parameters of interest, mainly a learning parameter. Due to the increased performance along with computational simplicity and robustness, the scheme is suitable to sample-by-sample identification of olfactory sensory data and can be easily adapted to hierarchical processing with other sensory data in real-time.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

9. Assembly neural network with nearest-neighbor recognition algorithm

Creator:
Goltsev, Alexander , Húsek, Dušan, and Frolov, Alexander
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
Assembly neural network, unsupervised learning, binary Hebbian rule pattern recognition, texture segmentation, and classification
Language:
English
Description:
An assembly neural network based on the binary Hebbian rule is suggested for pattern recognition. The network consists of several sub-networks according to the number of classes to be recognized. Each sub-network consists of several neural columns according to the dimensionality of the signal space so that the value of each signal component is encoded by activity of adjacent neurons of the column. A new recognition algorithm is presented which realizes the nearest-neighbor method in the assembly neural network. Computer simulation of the network is performed. The model is tested on a texture segmentation task. The experiments have demonstrated that the network is able to segment reasonably real-world texture images.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public

10. Automatic classification of agricultural grains: comparison of neural networks

Creator:
Kayabasi, Ahmet , Toktas, Abdurrahim , Sabanci, Kadir , and Yigit, Enes
Format:
bez média and svazek
Type:
model:article and TEXT
Subject:
classification, agricultural grains, wheat grains, neural networks, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM)
Language:
English
Description:
In this study, applications of well-known neural networks such as artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) for wheat grain classification into three species are comparatively presented. The species of wheat grains which are Kama (#70), Rosa (#70) and Canadian (#70) are designated as outputs of neural network models. The classification is carried out through data of wheat grains (#210) acquired using X-ray technique. The data set includes seven grain's geometric parameters: Area, perimeter, compactness, length, width, asymmetry coefficient and groove length. The neural networks input with the geometric parameters are trained through 189 wheat grain data and their accuracies are tested via 21 data. The performance of neural network models is compared to each other with regard to their accuracy, efficiency and convenience. For testing data, the ANN, ANFIS and SVM models numerically calculate the outputs with mean absolute error (MAE) of 0.014, 0.018 and 0.135, and classify the grains with accuracy of 100 %, 100% and 95.23 %, respectively. Furthermore, data of 210 grains is synthetically increased to 3210 in order to investigate the proposed models under big data. It is seen that the models are more successful if the size of data is increased, as well. These results point out that the neural networks can be successfully applied to classification of agricultural grains whether they are properly modelled and trained.
Rights:
http://creativecommons.org/publicdomain/mark/1.0/ and policy:public