Fuzzy pattern matching technique represents a group of fuzzy methods for supervised fuzzy patternrecognition. It has a number of advantages over other patternrecognition methods, including simpler methods of feature...
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ISBN:
(纸本)0780385667
Fuzzy pattern matching technique represents a group of fuzzy methods for supervised fuzzy patternrecognition. It has a number of advantages over other patternrecognition methods, including simpler methods of feature selection or ability to learn in real time environments, but its main drawback is it is not able to model the correlation between features, since fuzzy pattern matching assumes non interactivity between them. This paper presents an attempt to extend this technique to deal with this kind of features. To show the accuracy of the proposed solution, we present the results obtained in a simulated data set (an extension of the xor problem) and a real data set (the Wisconsin Breast Cancer data set).
The extraction of accurate facial landmarks play an exceedingly significant role for recognition of facial expressions. This paper proffers an approach in order for recognition of varied expressions of the faces by in...
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This book constitutes the refereed proceedings of the 4th internationalconference on patternrecognition and Machine Intelligence, PReMI 2011, held in Moscow, Russia in June/July 2011. The 65 revised papers presented...
ISBN:
(数字)9783642217869
ISBN:
(纸本)9783642217852
This book constitutes the refereed proceedings of the 4th internationalconference on patternrecognition and Machine Intelligence, PReMI 2011, held in Moscow, Russia in June/July 2011. The 65 revised papers presented together with 5 invited talks were carefully reviewed and selected from 140 submissions. The papers are organized in topical sections on patternrecognition and machine learning; image analysis; image and video information retrieval; natural language processing and text and data mining; watermarking, steganography and biometrics; softcomputing and applications; clustering and network analysis; bio and chemo analysis; and document image processing.
Protein fold patternrecognition has been one of the most challenging problems in biology during the last 40 years. Recently due to the vast improvement in machine learning and patternrecognition methods many compute...
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ISBN:
(纸本)9781424453306
Protein fold patternrecognition has been one of the most challenging problems in biology during the last 40 years. Recently due to the vast improvement in machine learning and patternrecognition methods many computer scientists have applied these methods to solve this problem. However, protein folding problem is much more complicated than ordinary machine learning problems because of its natural complexity imposed by the high dimensionality of feature space and diversity of different protein fold classes. To deal with such a challenging problem, we use an ensemble classifier model by applying MLP and RBF Neural Networks and Bayesian ensemble method. Also we have used the Laplace estimation method in order to smooth confusion matrices of the base classifiers. Experimental results imply that RBF Neural Network holds better Correct Classification Rate (CCR) compared to other common classification methods such as MLP networks. Our experiments also show that the Bayesian fusion method can improve the correct classification rate of proteins up to 20% with the final CCR of 59% by reducing both bias and variance error of the RBF classifiers, on a benchmark dataset containing 27 SCOP folds.
Previous studies have shown that choosing a different sample rate or signature point count provides better accuracy in online signature verification systems. However, the sampling rate that minimizes the error may var...
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ISBN:
(纸本)9781728175591
Previous studies have shown that choosing a different sample rate or signature point count provides better accuracy in online signature verification systems. However, the sampling rate that minimizes the error may vary on the database and signer levels. In this work, we studied the effect of choosing individual sampling frequencies for each signer and proposed a signature verification system based on signer-dependent sampling frequency. The system was tested on five different databases, using several features and preprocessing methods. Results showed accuracy improvement in 70% of the overall 500 tests and 92% in the chosen system where z-normalization and six samples used in the preprocessing phase.
Invariant descriptor for shape and texture image recognition usage is an essential branch of patternrecognition. It is made up of techniques that aim at extracting information from shape images via human knowledge an...
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ISBN:
(纸本)9781479934003
Invariant descriptor for shape and texture image recognition usage is an essential branch of patternrecognition. It is made up of techniques that aim at extracting information from shape images via human knowledge and works. The descriptors need to have strong Local Binary pattern (LBP) in order to encode the information distinguishing them. Local Binary pattern (LBP) ensures encoding global and local information and scaling invariance by introducing a look-up table to reflect the uniformity structure of an object. It is needed as the edge direction matrices (EDMS) only apply global invariant descriptor which employs first and secondary order relationships. The main objective of this paper is the need of improved recognition capabilities which achieved by the combining LBP and EDMS. Working together, these two descriptors will add advantages to the program and enable the researcher to investigate the weaknesses of each one. Two classifiers are used: multi-layer neural network and random forest. The techniques used in this paper are compared with Gray-Level Co-occurrence matrices (GLCM-EDMS) and Scale Invariant Feature Transform (SIFT) by using two benchmark dataset: MPEG-7 CE-Shape-1 for shape and Arabic calligraphy for texture. The experiments have shown the superiority of the introduced descriptor over the GLCMEDMS and the SIFT.
This paper describes how techniques from the discipline of neuro-fuzzy and softcomputing techniques can be used, in conjunction with methodologies from patternrecognition and digital signal processing, to effectivel...
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This paper describes how techniques from the discipline of neuro-fuzzy and softcomputing techniques can be used, in conjunction with methodologies from patternrecognition and digital signal processing, to effectively perform speech data classification. In particular, we have applied the proposed method to automatic speaker recognition and achieved satisfactory results.
Human facial behaviour recognition can be defined as the process of identifying human internal feelings or mood from the classification of facial expression and gesture. Human facial expression and gesture recognition...
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ISBN:
(纸本)9781479984886
Human facial behaviour recognition can be defined as the process of identifying human internal feelings or mood from the classification of facial expression and gesture. Human facial expression and gesture recognition have a many real world applications such as Human Machine Intelligent Interaction(HMII), Smart rooms, Advance Driver Assistance Systems(ADAS), Intelligent Robotics, Monitoring and Surveillance, Gaming, Research on pain and depression, Health support appliances. Facial Expression recognition is challenging problem up till now because of many reasons, moreover, it consists of three sub challenging tasks face detection, facial feature extraction and expression classification. softcomputing is a computer science field that applies to the problem whose solution is unpredictable or inexact. Digital image processing works effectively together with softcomputing techniques to improve efficiency of recognizing human facial behaviour through machine. This paper gives a review on the mechanisms of human facial behavior recognition using softcomputing techniques, which includes a brief detail on framework, literature survey and key issues in facial behaviour recognition using softcomputing.
This paper aims to provide combination of multiple prediction models using different strategies including ensemble selection, voting, stacking and multi-schemes to design a model capable of predicting oil prices accur...
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ISBN:
(纸本)9781479959341
This paper aims to provide combination of multiple prediction models using different strategies including ensemble selection, voting, stacking and multi-schemes to design a model capable of predicting oil prices accurately. Daily data from 1999 to 2012 with 14 variables were used, which were further divided into 10 sub-datasets according to various attribute selection methods. Four groups of training and testing were examined. Experimental results conclude that performance of the combination model works better than author's previous work and ensemble selection outperforms other combination methods.
This book constitutes the refereed proceedings of the international Second international Multi-conference on Artificial Intelligence Technology, M-CAIT 2013, held in Shah Alam, in August 2013. The 25 revised full pape...
ISBN:
(数字)9783642405679
ISBN:
(纸本)9783642405662;9783642405679
This book constitutes the refereed proceedings of the international Second international Multi-conference on Artificial Intelligence Technology, M-CAIT 2013, held in Shah Alam, in August 2013. The 25 revised full papers presented were carefully reviewed and selected from 110 submissions. M-CAIT 2013 hosted four special tracks in a single event: Intelligence Computation on pattern Analysis and Robotics (ICPAIR 2013), Data Mining and Optimization (DMO 2013), Semantic Technology and Information Retrieval (STAIR 2013) and Industrial computing & Applied Informatics (IComp 2013). The papers address issues of state-of-the-art research, development, implementation and applications within the four focus areas in CAIT: patternrecognition, data mining and optimization, knowledge technology and industrial computing.
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