Feedback Coding for Efficient Interactive Machine Learning

When training machine learning systems, the most basic scenario consists of the learning algorithm operating on a fixed batch of data, provided in its entirety before training. However, there are a large number of applications where there lies a choice in which data points are selected for labeling, and where this choice can be made “on the fly” after each selected data point is labeled. In such interactive machine learning (IML) systems, it is possible to train a model with far fewer labels than would be required with random sampling. In this thesis, we identify and model query structures in IML to develop direct information maximization solutions as well as approximations that allow for computationally efficient query selection. To do so, we frame IML as a feedback communications problem and directly apply principles and tools from coding theory to design and analyze new interaction selection algorithms. First, we directly apply a recently developed feedback coding scheme to sequential human-computer interaction systems. We then identify simplifying query structures to develop approximate methods for efficient, informative query selection in interactive ordinal embedding construction and preference learning systems. Finally, we combine the direct application of feedback coding with approximate information maximization to design and analyze a general active learning algorithm, which we study in detail for logistic regression.Ph.D

Statistical Data Modeling and Machine Learning with Applications

The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties

Machine Learning Methods with Noisy, Incomplete or Small Datasets

In many machine learning applications, available datasets are sometimes incomplete, noisy or affected by artifacts. In supervised scenarios, it could happen that label information has low quality, which might include unbalanced training sets, noisy labels and other problems. Moreover, in practice, it is very common that available data samples are not enough to derive useful supervised or unsupervised classifiers. All these issues are commonly referred to as the low-quality data problem. This book collects novel contributions on machine learning methods for low-quality datasets, to contribute to the dissemination of new ideas to solve this challenging problem, and to provide clear examples of application in real scenarios

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Novel Transfer Learning Approaches forImproving Brain Computer Interfaces

Despite several recent advances, most of the electroencephalogram(EEG)-based brain-computer interface (BCI) applications are still limited to the laboratory due to their long calibration time. Due toconsiderable inter-subject/inter-session and intra-session variations, atime-consuming and fatiguing calibration phase is typically conductedat the beginning of each new session to acquire sufficient labelled train-ing data to train the subject-specific BCI model.This thesis focuses on developing reliable machine learning algorithmsand approaches that reduce BCI calibration time while keeping accu-racy in an acceptable range. Calibration time could be reduced viatransfer learning approaches where data from other sessions or sub-jects are mined and used to compensate for the lack of labelled datafrom the current user or session. In BCI, transfer learning can beapplied on either raw EEG, feature or classification domains.In this thesis, firstly, a novel weighted transfer learning approach isproposed in the classification domain to improve the MI-based BCIperformance when only few subject-specific trials are available fortraining.Transfer learning techniques should be applied in a different domainbefore the classification domain to improve the classification accuracyfor subjects whom their subject-specific features for different classesare not separable. Thus, secondly, this thesis proposes a novel regu-larized common spatial patterns framework based on dynamic timewarping and transfer learning (DTW-R-CSP) in raw EEG and featuredomains.In previous transfer learning approaches, it is hypothesised that thereare enough labelled trials available from the previous subjects or ses-sions. However, in the case when there are no labelled trials available from other subjects or sessions, domain adaptation transfer learningcould potentially mitigate problems of having small training size byreducing variations between the testing and training trials. Thus, todeal with non-stationarity between training and testing trials, a novelensemble adaptation framework with temporal alignment is proposed

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Adaptation and Control State Detection Techniques for Brain-Computer Interfaces

Ph.DDOCTOR OF PHILOSOPH

Connected Attribute Filtering Based on Contour Smoothness

A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform