Adaptive Algorithms For Classification On High-Frequency Data Streams: Application To Finance

Mención Internacional en el título de doctorIn recent years, the problem of concept drift has gained importance in the financial domain. The succession of manias, panics and crashes have stressed the nonstationary nature and the likelihood of drastic structural changes in financial markets. The most recent literature suggests the use of conventional machine learning and statistical approaches for this. However, these techniques are unable or slow to adapt to non-stationarities and may require re-training over time, which is computationally expensive and brings financial risks. This thesis proposes a set of adaptive algorithms to deal with high-frequency data streams and applies these to the financial domain. We present approaches to handle different types of concept drifts and perform predictions using up-to-date models. These mechanisms are designed to provide fast reaction times and are thus applicable to high-frequency data. The core experiments of this thesis are based on the prediction of the price movement direction at different intraday resolutions in the SPDR S&P 500 exchange-traded fund. The proposed algorithms are benchmarked against other popular methods from the data stream mining literature and achieve competitive results. We believe that this thesis opens good research prospects for financial forecasting during market instability and structural breaks. Results have shown that our proposed methods can improve prediction accuracy in many of these scenarios. Indeed, the results obtained are compatible with ideas against the efficient market hypothesis. However, we cannot claim that we can beat consistently buy and hold; therefore, we cannot reject it.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Gustavo Recio Isasi.- Secretario: Pedro Isasi Viñuela.- Vocal: Sandra García Rodrígue

Evaluation and analysis of hybrid intelligent pattern recognition techniques for speaker identification

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content (i.e. text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system. A novel approach towards speaker identification is developed using wavelet analysis, and multiple neural networks including Probabilistic Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state- of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA). Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear. Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identi¯cation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that the proposed scheme is one of the best candidates for the fusion of face and voice due to its low computational time and high recognition accuracy

Content-prioritised video coding for British Sign Language communication.

Video communication of British Sign Language (BSL) is important for remote interpersonal communication and for the equal provision of services for deaf people. However, the use of video telephony and video conferencing applications for BSL communication is limited by inadequate video quality. BSL is a highly structured, linguistically complete, natural language system that expresses vocabulary and grammar visually and spatially using a complex combination of facial expressions (such as eyebrow movements, eye blinks and mouth/lip shapes), hand gestures, body movements and finger-spelling that change in space and time. Accurate natural BSL communication places specific demands on visual media applications which must compress video image data for efficient transmission. Current video compression schemes apply methods to reduce statistical redundancy and perceptual irrelevance in video image data based on a general model of Human Visual System (HVS) sensitivities. This thesis presents novel video image coding methods developed to achieve the conflicting requirements for high image quality and efficient coding. Novel methods of prioritising visually important video image content for optimised video coding are developed to exploit the HVS spatial and temporal response mechanisms of BSL users (determined by Eye Movement Tracking) and the characteristics of BSL video image content. The methods implement an accurate model of HVS foveation, applied in the spatial and temporal domains, at the pre-processing stage of a current standard-based system (H.264). Comparison of the performance of the developed and standard coding systems, using methods of video quality evaluation developed for this thesis, demonstrates improved perceived quality at low bit rates. BSL users, broadcasters and service providers benefit from the perception of high quality video over a range of available transmission bandwidths. The research community benefits from a new approach to video coding optimisation and better understanding of the communication needs of deaf people

Contributions to unsupervised and supervised learning with applications in digital image processing

311 p. : il.[EN]This Thesis covers a broad period of research activities with a commonthread: learning processes and its application to image processing. The twomain categories of learning algorithms, supervised and unsupervised, have beentouched across these years. The main body of initial works was devoted tounsupervised learning neural architectures, specially the Self Organizing Map.Our aim was to study its convergence properties from empirical and analyticalviewpoints.From the digital image processing point of view, we have focused on twobasic problems: Color Quantization and filter design. Both problems have beenaddressed from the context of Vector Quantization performed by CompetitiveNeural Networks. Processing of non-stationary data is an interesting paradigmthat has not been explored with Competitive Neural Networks. We have statesthe problem of Non-stationary Clustering and related Adaptive Vector Quantizationin the context of image sequence processing, where we naturally havea Frame Based Adaptive Vector Quantization. This approach deals with theproblem as a sequence of stationary almost-independent Clustering problems.We have also developed some new computational algorithms for Vector Quantizationdesign.The works on supervised learning have been sparsely distributed in time anddirection. First we worked on the use of Self Organizing Map for the independentmodeling of skin and no-skin color distributions for color based face localization. Second, we have collaborated in the realization of a supervised learning systemfor tissue segmentation in Magnetic Resonance Imaging data. Third, we haveworked on the development, implementation and experimentation with HighOrder Boltzmann Machines, which are a very different learning architecture.Finally, we have been working on the application of Sparse Bayesian Learningto a new kind of classification systems based on Dendritic Computing. This lastresearch line is an open research track at the time of writing this Thesis

The application of artificial neural networks to interpret acoustic emissions from submerged arc welding

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Automated fusion welding processes play a fundamental role in modern manufacturing industries. The proliferation of joint geometries together with the large permutation of associated process variable configurations has given rise to research into complex system modelling and control strategies. Many of these techniques have involved monitoring of not only the electrical characteristics of the process but visual and acoustic information. Acoustic information derived from certain welding processes is well documented as it is an established fact that skilled manual welders utilise such information as an aid to creating an optimum weld. The experimental investigation presented in this thesis is dedicated to the feasibility of monitoring airborne acoustic emissions of Submerged Arc Welding (SAW) for diagnostic and real time control purposes. The experimental method adopted for this research takes a cybernetic approach to data processing and interpretation in an attempt to replicate the robustness of human biological functions. A custom designed audio hardware system was used to analyse signals obtained from bead on mild steel plate fusion welds. Time and frequency domains were used in an attempt to establish salient characteristics or identify the signatures associated with changes of the process variables. The featured parameters were voltage / current and weld travel speed, due to their ease of validation. However, consideration has also been given to weld defect prediction due to process instabilities. As the data proved to be highly correlated and erratic when subjected to off line statistical analysis, extensive investigation was given to the application of artificial neural networks to signal processing and real time control scenarios. As a consequence, a dedicated neural based software system was developed, utilising supervised and unsupervised neural techniques to monitor the process. The research was aimed at proving the feasibility of monitoring the electrical process parameters and stability of the welding process in real time. It was shown to be possible, by the exploitation of artificial neural networks, to generate a number of monitoring parameters indicative of the welding process state. The limitations of the present neural method and proposed developments are discussed, together with an overview of applied neural network technology and its impact on artificial intelligence and robotic control. Further developments are considered together with recommendations for future areas of research

Image synthesis based on a model of human vision

Modern computer graphics systems are able to construct renderings of such high quality that viewers are deceived into regarding the images as coming from a photographic source. Large amounts of computing resources are expended in this rendering process, using complex mathematical models of lighting and shading. However, psychophysical experiments have revealed that viewers only regard certain informative regions within a presented image. Furthermore, it has been shown that these visually important regions contain low-level visual feature differences that attract the attention of the viewer. This thesis will present a new approach to image synthesis that exploits these experimental findings by modulating the spatial quality of image regions by their visual importance. Efficiency gains are therefore reaped, without sacrificing much of the perceived quality of the image. Two tasks must be undertaken to achieve this goal. Firstly, the design of an appropriate region-based model of visual importance, and secondly, the modification of progressive rendering techniques to effect an importance-based rendering approach. A rule-based fuzzy logic model is presented that computes, using spatial feature differences, the relative visual importance of regions in an image. This model improves upon previous work by incorporating threshold effects induced by global feature difference distributions and by using texture concentration measures. A modified approach to progressive ray-tracing is also presented. This new approach uses the visual importance model to guide the progressive refinement of an image. In addition, this concept of visual importance has been incorporated into supersampling, texture mapping and computer animation techniques. Experimental results are presented, illustrating the efficiency gains reaped from using this method of progressive rendering. This visual importance-based rendering approach is expected to have applications in the entertainment industry, where image fidelity may be sacrificed for efficiency purposes, as long as the overall visual impression of the scene is maintained. Different aspects of the approach should find many other applications in image compression, image retrieval, progressive data transmission and active robotic vision

Video coding for compression and content-based functionality

The lifetime of this research project has seen two dramatic developments in the area of digital video coding. The first has been the progress of compression research leading to a factor of two improvement over existing standards, much wider deployment possibilities and the development of the new international ITU-T Recommendation H.263. The second has been a radical change in the approach to video content production with the introduction of the content-based coding concept and the addition of scene composition information to the encoded bit-stream. Content-based coding is central to the latest international standards efforts from the ISO/IEC MPEG working group. This thesis reports on extensions to existing compression techniques exploiting a priori knowledge about scene content. Existing, standardised, block-based compression coding techniques were extended with work on arithmetic entropy coding and intra-block prediction. These both form part of the H.263 and MPEG-4 specifications respectively. Object-based coding techniques were developed within a collaborative simulation model, known as SIMOC, then extended with ideas on grid motion vector modelling and vector accuracy confidence estimation. An improved confidence measure for encouraging motion smoothness is proposed. Object-based coding ideas, with those from other model and layer-based coding approaches, influenced the development of content-based coding within MPEG-4. This standard made considerable progress in this newly adopted content based video coding field defining normative techniques for arbitrary shape and texture coding. The means to generate this information, the analysis problem, for the content to be coded was intentionally not specified. Further research work in this area concentrated on video segmentation and analysis techniques to exploit the benefits of content based coding for generic frame based video. The work reported here introduces the use of a clustering algorithm on raw data features for providing initial segmentation of video data and subsequent tracking of those image regions through video sequences. Collaborative video analysis frameworks from COST 21 l qual and MPEG-4, combining results from many other segmentation schemes, are also introduced