Two of the prime open-source environments available for machine/statistical learning in data mining and knowledge discovery are the software packages Weka and R which have emerged from the machine learning and statistics communities, respectively. To make the different sets of tools from both environments available in a single unified system, an R package RWeka is suggested which interfaces Weka's functionality to R. With only a thin layer of (mostly R) code, a set of general interface generators is provided which can set up interface functions with the usual "R look and feel", re-using Weka's standardized interface of learner classes (including classifiers, clusterers, associators, filters, loaders, savers, and stemmers) with associated methods. / Series: Research Report Series / Department of Statistics and Mathematics

This thesis considers the problem of feature selection when the number of predictors is larger than the number of samples. The performance of supersaturated design (SSD) working with least absolute shrinkage and selection operator (LASSO) is studied in this setting. In order to achieve higher feature selection correctness, self-voting LASSO is implemented to select the tuning parameter while approximately optimize the probability of achieving Sign Correctness. Furthermore, we derive the probability of achieving Direction Correctness, and extend the self-voting LASSO to multi-task self-voting LASSO, which has a group screening effect for multiple tasks.

The following two studies examined the influence of spatial regularities on our ability to learn and predict frequencies and sequences of events. Research into statistical and sequence learning has demonstrated that we can learn the statistical properties of events and use this knowledge to make predictions about future events. Research has also demonstrated that redundant spatial features associated with events can influence our ability to respond to and discriminate between different stimuli. The goal of this thesis was to test whether redundant spatial features could influence our ability to notice non-spatial regularities in an environment. Using a computerized version of the children’s game ‘rock-paper-scissors’ (RPS), undergraduates were instructed to win as often as possible against a computer that played varying strategies. For each strategy, the computer’s plays were either presented with spatial regularity (i.e., ‘rock’ would always appear on the left of the screen, ‘paper’ in the middle, and ‘scissors’ on the right) or without spatial regularity (i.e., the items were equally likely to appear in any of the three screen locations). The results showed that, although irrelevant to the task itself, spatial regularities had a moderate influence when participants learned to exploit easy strategies, and a more pronounced influence when learning to exploit harder strategies. This research suggests that redundant spatial features can influence our ability to learn and represent distributions of events.

This project investigated the statistical learning of musical expectancy. As a secondary goal, the effects of the perceptual properties of tone set familiarity (Western vs. Bohlen-Pierce) and textural complexity (melody vs. harmony) on the robustness of that learning process were assessed. A series of five experiments was conducted, varying in terms of these perceptual properties, the grammatical structure used to generate musical sequences, and the methods used to measure musical expectancy. Results indicated that expectancies can indeed be developed following statistical learning, particularly for materials composed from familiar tone sets. Moreover, some expectancy effects were observed in the absence of the ability to successfully discriminate between grammatical and ungrammatical items. The effect of these results on our current understanding of expectancy formation is discussed, as is the appropriateness of the behavioural methods used in this research.

It is argued that infants build a foundation for learning about the world through their incidental acquisition of the spatial and temporal regularities surrounding them. A challenge is that learning occurs across multiple contexts whose statistics can greatly differ. Two artificial language studies with 12-month-olds demonstrate that infants come prepared to parse statistics across contexts using the temporal and perceptual features that distinguish one context from another. These results suggest that infants can organize their statistical input with a wider range of features that typically considered. Possible attention, decision making, and memory mechanisms are discussed.

Learners are exquisitely attuned to statistical information in their language input. We tested how prior experience impacts such sensitivity, particularly whether prior experience serves as a bootstrap by enabling acquisition of more complex structure. Experiments 1 and 2 tested whether giving adult learners experience with adjacent category-dependencies in an artificial language facilitates subsequent learning of a novel language containing more complex nonadjacent dependencies. Prior experience had a facilitating effect, both when it preceded exposure to the nonadjacent language by just a few minutes (Experiment 1), and also by 24 hours (Experiment 2). Prior experience with the vocabulary and prosodic characteristics of the language did not facilitate more complex learning. Experiments 3 and 4 tested whether infants also benefit from prior experience in learning nonadjacent dependencies between categories. While 12-month-olds learn adjacent dependencies between word categories (Gómez & Lakusta, 2004), they do not track nonadjacent word dependencies until 15 months (Gómez & Maye, 2005). We asked whether experience with adjacent word-category dependencies enables 12-month-olds to generalize these relations to nonadjacent occurrences. Infants were familiarized to an artificial language containing adjacent category dependencies, and were habituated to strings in which those dependencies were nonadjacent. Infants dishabituated to strings containing violations of the nonadjacent dependencies when the dependencies had been adjacent during previous familiarization (Experiment 3), and when they were novel (Experiment 4). Infants familiarized to a language lacking co-occurrence restrictions, but otherwise matched to the experimental language, failed to become sensitive to the nonadjacent category dependencies during habituation. These findings demonstrate that prior experience can bootstrap acquisition of more complex language structure.

The mobile telecommunication industry has experienced a very rapid growth in the recent past. This has resulted in significant technological and architectural evolution in the wireless networks. The expansion and the heterogenity of these networks have made their operational cost more and more important. Typical faults in these networks may be related to equipment breakdown and inappropriate planning and configuration. In this context, automated troubleshooting in wireless networks receives a growing importance, aiming at reducing the operational cost and providing high-quality services for the end-users. Automated troubleshooting can reduce service breakdown time for the clients, resulting in the decrease in client switchover to competing network operators. The Radio Access Network (RAN) of a wireless network constitutes its biggest part. Hence, the automated troubleshooting of RAN of the wireless networks is very important. The troubleshooting comprises the isolation of the faulty cells (fault detection), identifying the causes of the fault (fault diagnosis) and the proposal and deployement of the healing action (solution deployement). First of all, in this thesis, the previous work related to the troubleshooting of the wireless networks has been explored. It turns out that the fault detection and the diagnosis of wireless networks have been well studied in the scientific literature. Surprisingly, no significant references for the research work related to the automated healing of wireless networks have been reported. Thus, the aim of this thesis is to describe my research advances on "Automated healing of LTE wireless networks using statistical learning". We focus on the faults related to Radio Resource Management (RRM) parameters. This thesis explores the use of statistical learning for the automated healing process. In this context, the effectiveness of statistical learning for automated RRM has been investigated. This is achieved by modeling the functional relationships between the RRM parameters and Key Performance Indicators (KPIs). A generic automated RRM architecture has been proposed. This generic architecture has been used to study the application of statistical learning approach to auto-tuning and performance monitoring of the wireless networks. The use of statistical learning in the automated healing of wireless networks introduces two important diculties: Firstly, the KPI measurements obtained from the network are noisy, hence this noise can partially mask the actual behaviour of KPIs. Secondly, these automated healing algorithms are iterative. After each iteration the network performance is typically evaluated over the duration of a day with new network parameter settings. Hence, the iterative algorithms should achieve their QoS objective in a minimum number of iterations. Automated healing methodology developped in this thesis, based on statistical modeling, addresses these two issues. The automated healing algorithms developped are computationaly light and converge in a few number of iterations. This enables the implemenation of these algorithms in the Operation and Maintenance Center (OMC) in the off-line mode. The automated healing methodolgy has been applied to 3G Long Term Evolution (LTE) use cases for healing the mobility and intereference mitigation parameter settings. It has been observed that our healing objective is achieved in a few number of iterations. An automated healing process using the sequential optimization of interference mitigation and packet scheduling parameters has also been investigated. The incorporation of the a priori knowledge into the automated healing process, further reduces the number of iterations required for automated healing. Furthermore, the automated healing process becomes more robust, hence, more feasible and practical for the implementation in the wireless networks.

A simulated learning hierarchical architecture for vector classification is presented. The hierarchy used homogeneous scripted classifiers, maintaining similarity tables, and selforganising maps for the input. The scripted classifiers produced output, and guided learning with permutable script instruction tables. A large space of parametrised script instructions was created, from which many different combinations could be implemented. The parameter space for the script instruction tables was tuned using a genetic algorithm with the goal of optimizing the networks ability to predict class labels for bit pattern inputs. The classification system, known as Dura, was presented with various visual classification problems, such as: detecting overlapping lines, locating objects, or counting polygons. The network was trained with a random subset from the input space, and was then tested over a uniformly sampled subset. The results showed that Dura could successfully classify these and other problems. The optimal scripts and parameters were analysed, allowing inferences about which scripted operations were important, and what roles they played in the learning classification system. Further investigations were undertaken to determine Dura's performance in the presence of noise, as well as the robustness of the solutions when faced with highly stochastic training sequences. It was also shown that robustness and noise tolerance in solutions could be improved through certain adjustments to the algorithm. These adjustments led to different solutions which could be compared to determine what changes were responsible for the increased robustness or noise immunity. The behaviour of the genetic algorithm tuning the network was also analysed, leading to the development of a super solutions cache, as well as improvements in: convergence, fitness function, and simulation duration. The entire network was simulated using a program written in C++ using FLTK libraries for the graphical user interface.

This project investigated the statistical learning of musical expectancy. As a secondary goal, the effects of the perceptual properties of tone set familiarity (Western vs. Bohlen-Pierce) and textural complexity (melody vs. harmony) on the robustness of that learning process were assessed. A series of five experiments was conducted, varying in terms of these perceptual properties, the grammatical structure used to generate musical sequences, and the methods used to measure musical expectancy. Results indicated that expectancies can indeed be developed following statistical learning, particularly for materials composed from familiar tone sets. Moreover, some expectancy effects were observed in the absence of the ability to successfully discriminate between grammatical and ungrammatical items. The effect of these results on our current understanding of expectancy formation is discussed, as is the appropriateness of the behavioural methods used in this research.

The following two studies examined the influence of spatial regularities on our ability to learn and predict frequencies and sequences of events. Research into statistical and sequence learning has demonstrated that we can learn the statistical properties of events and use this knowledge to make predictions about future events. Research has also demonstrated that redundant spatial features associated with events can influence our ability to respond to and discriminate between different stimuli. The goal of this thesis was to test whether redundant spatial features could influence our ability to notice non-spatial regularities in an environment. Using a computerized version of the children’s game ‘rock-paper-scissors’ (RPS), undergraduates were instructed to win as often as possible against a computer that played varying strategies. For each strategy, the computer’s plays were either presented with spatial regularity (i.e., ‘rock’ would always appear on the left of the screen, ‘paper’ in the middle, and ‘scissors’ on the right) or without spatial regularity (i.e., the items were equally likely to appear in any of the three screen locations). The results showed that, although irrelevant to the task itself, spatial regularities had a moderate influence when participants learned to exploit easy strategies, and a more pronounced influence when learning to exploit harder strategies. This research suggests that redundant spatial features can influence our ability to learn and represent distributions of events.