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A specific issue in the voice password system is addressed in this paper: When the text content of target speaker's enrollment password has been already known by imposters, they can do a well-behaved impersonation using the same text content as the target speaker. This results in a much higher false acceptance than the traditional voice password system. N-gram based nearest neighbor algorithm is proposed...
The paper introduces novel machine learning (data mining) algorithm called Adaptive Local Hyperplane (ALH) and it presents its application in solving regression problems. ALH algorithm has recently shown extremely good results in classification, and it is adopted for solving regression tasks here. It is a local margin maximizing algorithm in the original, weighted, input space blending a Nearest Neighbors...
In this paper, the vision-based hand movement recognition problem is formulated for the universe of discourse of the Brazilian Sign Language. In order to analyze this specific domain we have used the artificial neural networks models based on distance, including neural-fuzzy models. The experiments explored here show the usefulness of these models to extract helpful knowledge about the classes of...
Neural networks methodology is a tool to get potential energy surface (PES) in cases where there is too much dispersion of data; hence, a binding energy fitting can be found with this methodology on asphaltene-asphaltene molecular interaction. A data distribution of intermolecular pair potential (UAA) interaction in a vacuum between two molecular asphaltenes systems using compass classical force field...
In this paper, an analog current mode implementation of a neuron circuit capable of performing real Gaussian neighborhood taper learning is presented. The neuron cell is compacted with a reusable multiplier that can function as squarer and multiplier for Euclidean and topological distances calculation as well as for Gaussian function characteristics with adjustable learning rate. A four-neuron self-organizing...
At present, the application of neural network technology in the evaluation of the distance learning support service. The reason lies in the difficulty to find high quality training samples for neural network self-learning and the training lacks strict scientific experimental design. This paper adopt Uniform Design Method (UDM) to design representative, uniformity and large-scale samples. And then...
This research develops a weighted evolving fuzzy neural network for electricity demand forecasting in Taiwan. This study modifies the evolving fuzzy neural network framework (EFuNN framework) and adopts a weighted factor to calculate the importance of each factor among these different rules. In addition, an exponential transfer function (exp(-D)) is employed to transfer the distance of any two factors...
Stock turning points detection is a very interesting subject arising in numerous financial and economic planning problems. In this paper, evolving neural network model with dynamic time warping piecewise linear representation system for stock turning points detection is presented. The piecewise linear representation method is able to generate numerous stocks turning points from the historic data base,...
Due to the quality of service issues that wireless communications for data transmission need to ensure, we use the SOM neural network for QoS pattern space convergence, and apply cluster results to the shortest path algorithm in sensor networks. First, we measures the packet loss rate in different communication distance and the noise power density in underground straight laneway. From these we obtain...
A new algorithm, Laplacian minmax discriminant projection (LMMDP), is proposed in this paper for supervised dimensionality reduction. LMMDP aims at learning a linear transformation which is an extension of linear discriminant analysis (LDA). Specifically, we define the within-class scatter and the between-class scatter using similarities which are based on pairwise distances in sample space. After...
This paper presents a new learning algorithm for feedforward neural networks. This algorithm uses the vigilance parameter to generate the hidden layer neurons. This process improves the initial weight problem and the adaptive neurons of the hidden layer. The proposed approach is based on combined unsupervised and supervised learning. In this algorithm, the weights between input and hidden layers are...
One knowledge discovery problem in the rapid response setting is the cost of learning which patterns are indicative of a threat. This typically involves a detailed follow-through, such as review of documents and information by a skilled analyst, or detailed examination of a vehicle at a border crossing point, in deciding which suspicious vehicles require investigation. Assessing various strategies...
Contact map is the simplified, 2D representation of protein spatial structure. Contact map prediction is an intermediate step to predict protein 3D structure. Ensemble learning-based model is a collection of learners that is more accurate than a single learner. In this paper we propose an ensemble learning method for contact map prediction. Results show that a considerable performance improvement...
The target of this project is to propose and implement incremental system for multi languages linguistic command recognition in multi agent system MASS, based on client-server architecture. Preprocessing is realized with the aid of cepstral coefficients and classification is realized by modified MF Artmap. System allows remote parallel learning of various commands, their consecutive identification...
As one of the extensive applications of artificial neural network, BP algorithm has some shortcomings such as local optimum. In this paper, we propose a new method--TACO-BP algorithm to train neural network, which may overcome the shortcoming. Firstly, we give description about the TACO-BP. After experiments, we compare the performance between TACO-BPNN and BPNN. Lastly, we analyze the results of...
Rare category detection is the task of identifying examples from rare classes in an unlabeled data set. It is an open challenge in machine learning and plays key roles in real applications such as financial fraud detection, network intrusion detection, astronomy, spam image detection, etc. In this paper, we develop a new graph-based method for rare category detection named GRADE. It makes use of the...
Application-specific dissimilarity functions can be used for learning from a set of objects represented by pairwise dissimilarity matrices in this context. These dissimilarities may, however, suffer from various defects, e.g. when derived from a suboptimal optimization or by the use of non-metric or noisy measures. In this paper, we study procedures for refining such dissimilarities. These methods...
This paper deals with incremental classification and its particular application to invoice classification. An improved version of an already existant incremental neural network called IGNG (incremental growing neural gas) is used for this purpose. This neural network tries to cover the space of data by adding or deleting neurons as data is fed to the system. The improved version of the IGNG, called...
In this paper, generalized learning vector quantization (GLVQ) with local subspace classifier (LSC) is proposed for achieving high accuracy with a small memory requirement. In a training phase, the k-closest prototypes to an input training sample are moved by the same update rule of GLVQ for reducing the number of misclassification on training samples. In a test phase, a test sample is classified...
In the multi-attribute decision making theory (MADM), fuzzy multi-attribute decision making theory is an important aspect. And in practical problem, some index can't be expressed by certain number, just only expressed by linguistic words, under this situation, we must use fuzzy decision making method. This paper uses fuzzy LMS neural network to determinate weight, this method has the advantage of...
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