Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

An Introduction to MultiAgent Systems.

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

International Technology Roadmap for Semiconductors 2003の要求清浄度について － シリコンウエハ表面と雰囲気環境に要求される清浄度, 分析方法の現状について －

This chapter introduces the subject of statistical pattern recognition (SPR). It starts by considering how features are defined and emphasizes that the nearest neighbor algorithm achieves error rates comparable with those of an ideal Bayes’ classifier. The concepts of an optimal number of features, representativeness of the training data, and the need to avoid overfitting to the training data are stressed. The chapter shows that methods such as the support vector machine and artificial neural networks are subject to these same training limitations, although each has its advantages. For neural networks, the multilayer perceptron architecture and back-propagation algorithm are described. The chapter distinguishes between supervised and unsupervised learning, demonstrating the advantages of the latter and showing how methods such as clustering and principal components analysis fit into the SPR framework. The chapter also defines the receiver operating characteristic, which allows an optimum balance between false positives and false negatives to be achieved.

Statistical Pattern Recognition

Traditional feature extraction methods, such as Gabor filter and competitive coding, have been widely used in finger-knuckle-print (FKP) recognition. However, these methods focus on manually designed features which may not achieve satisfying results on FKP images. In order to solve this problem, a novel batch-normalized Convolutional Neural Network (CNN) architecture with data augmentation for FKP recognition is proposed. Firstly, a novel batch-normalized CNN is designed specifically for FKP recognition. Then, random histogram equalization is adopted as data augmentation here for training the CNN in FKP recognition. Meanwhile, batch-normalization is adopted to avoid overfitting during network training. Extensive experiments performed on the PolyU FKP database show that compared with traditional feature extraction method, the proposed method can not only extract more discriminative features, but also improve the accuracy of FKP recognition.

A Novel Finger-Knuckle-Print Recognition Based on Batch-Normalized CNN.

Biometric systems identify individuals by comparison of the individual biometric traits, such as the fingerprint patterns. In the literature, many relevant methods are based on the localization of a reference “pivot” point of the fingerprint, called principal singular point (PSP). Most of the time, the PSP is selected from the list of the estimated singular points (SPs) that are identified by specific local patterns of the fingerprint ridges, called cores and deltas. The challenge is to provide an automatic method capable to select the same PSP from different images of the same fingertip. In this paper, we propose a technique that estimates the position of all the singular points by processing the global structure of the ridges and extracting a specific set of features. The selection of the reference point from the candidate list is then obtained by processing the extracted features with computational intelligence classification techniques. Experiments show that the method is accurate and it can be applied on contact and contact-less image types.

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Measurement of the principal singular point in contact and contactless fingerprint images by using computational intelligence techniques

Soft biometric techniques can perform a fast and unobtrusive identification within a limited number of users, be used as a preliminary screening filter, or combined in order to increase the recognition accuracy of biometric systems. The weight is a soft biometric trait which offers a good compromise between distinctiveness and permanence, and is frequently used in forensic applications. However, traditional weight measurement techniques are time-consuming and have a low user acceptability. In this paper, we propose a method for a contactless, low-cost, unobtrusive, and unconstrained weight estimation from frame sequences representing a walking person. The method uses image processing techniques to extract a set of features from a pair of frame sequences captured by two cameras. Then, the features are processed using a computational intelligence approach, in order to learn the relations between the extracted characteristics and the weight of the person. We tested the proposed method using frame sequences describing eight different walking directions, and captured in uncontrolled light conditions. The obtained results show that the proposed method is feasible and can achieve a view-independent weight estimation, also without the need of computing a complex model of the body parts.

/pdf/weight-estimation-from-frame-sequences-using-computational-20teczujqt.pdf

Weight estimation from frame sequences using computational intelligence techniques

The diffusion of wearable and mobile devices for the acquisition and analysis of cardiac signals drastically increased the possible applicative scenarios of biometric systems based on electrocardiography (ECG) Moreover, such devices allow for comfortable and unconstrained acquisitions of ECG signals for relevant time spans of tens of hours, thus making these physiological signals particularly attractive biometric traits for continuous authentication applications In this context, recent studies showed that the QRS complex is the most stable component of the ECG signal, but the accuracy of the authentication degrades over time, due to significant variations in the patterns for each individual Adaptive techniques for automatic template update can therefore become enabling technologies for continuous authentication systems based on ECG characteristics

/pdf/adaptive-ecg-biometric-recognition-a-study-on-re-enrollment-53mhv1wnea.pdf

Adaptive ECG biometric recognition: a study on re-enrollment methods for QRS signals

The current cloud market features a multitude of cloud services that differ from one another in terms of functionality or of security/performance guarantees. Users wishing to use a cloud service for storing, processing, or sharing their data must be able to select the service that best matches their desiderata. In this paper, we propose a novel, user centric, brokering service for supporting users in the specification of requirements and enabling their evaluation against available cloud plans, assessing how much the different plans can satisfy the user's desiderata. Our brokering service allows users to specify their desiderata in an easy and intuitive way by using natural language expressions and high-level concepts. Fuzzy logic and fuzzy inference systems are adopted to quantitatively assess the compliance of cloud services with the users’ desiderata, and hence to help users in the cloud service selection process.

/pdf/a-fuzzy-based-brokering-service-for-cloud-plan-selection-4h5rqq9cvd.pdf

Vincenzo Piuri

Papers

A Novel Finger-Knuckle-Print Recognition Based on Batch-Normalized CNN.

Measurement of the principal singular point in contact and contactless fingerprint images by using computational intelligence techniques

Weight estimation from frame sequences using computational intelligence techniques

Adaptive ECG biometric recognition: a study on re-enrollment methods for QRS signals

A Fuzzy-Based Brokering Service for Cloud Plan Selection