Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

Meta-learning for informed model searches in Automated Machine Learning

Day by day, machine learning is changing our lives in ways we could not have imagined just 5 years ago. ML expertise is more and more requested and needed, though just a limited number of ML engineers are available on the job market, and their knowledge is always limited by an inherent characteristic of theirs: they are humans. This thesis explores the possibilities offered by meta-learning, a new field in ML that takes learning a level higher: models are trained on other models' training data, starting from features of the dataset they were trained on, inference times, obtained performances, to try to understand the relationship between a good model and the way it was obtained. The so-called metamodel was trained on data collected by OpenML, the largest ML metadata platform that's publicly available today. Datasets were analyzed to obtain meta-features that describe them, which were then tied to model performances in a regression task. The obtained metamodel predicts the expected performances of a given model type (e.g., a random forest) on a given ML task (e.g., classification on the UCI census dataset). This research was then integrated into a custom-made AutoML framework, to show how meta-learning is not an end in itself, but it can be used to further progress our ML research. Encoding ML engineering expertise in a model allows better, faster, and more impactful ML applications across the whole world, while reducing the cost that is inevitably tied to human engineers.

Learning information technology business in a changing industry landscape. Introducing Team Entrepreneurship in Renewing Bachelor Education in Renewing Bachelor Education in Information

Speed, uncertainty and complexity are increasing in the business world all the time. When knowledge and skills become quickly irrelevant, new challenges are set for information technology (IT) education. Meta-learning skills – learning how to learn rapidly - and innovation skills have become more essential than single technologies or other specific issues. The drastic changes in the information and communications technology (ICT) sector have caused a need to reconsider how IT Bachelor education in Universities of Applied Sciences should be organized and employed to cope with the change. The objective of the study was to evaluate how a new approach to IT Bachelor education, the ICT entrepreneurship study path (ICT-ESP) fits IT Bachelor education in a Finnish University of Applied Sciences. This kind of educational arrangement has not been employed elsewhere in the context of IT Bachelor education. The study presents the results of a four-year period during which IT Bachelor education was renewed in a Finnish University of Applied Sciences. The learning environment was organized into an ICT-ESP based on Nonaka’s knowledge theory and Kolb’s experiental learning. The IT students who studied in the ICT-ESP established a cooperative and learned ICT by running their cooperative at the University of Applied Sciences. The students (called team entrepreneurs) studied by reading theory in books and other sources of explicit information, doing projects for their customers, and reflecting in training sessions on what was learnt by doing and by studying the literature. Action research was used as the research strategy in this study. Empirical data was collected via theme-based interviews, direct observation, and participative observation. Grounded theory method was utilized in the data analysis and the theoretical sampling was used to guide the data collection. The context of the University of Applied Sciences provided a good basis for fostering team entrepreneurship. However, the results showed that the employment of the ICT-ESP did not fit into the IT Bachelor education well enough. The ICT-ESP was cognitively too tough for the team entrepreneurs because they had two different set of rules to follow in their studies. The conventional courses engaged lot of energy which should have been spent for professional development in the ICT-ESP. The amount of competencies needed in the ICT-ESP for professional development was greater than those needed for any other ways of studying. The team entrepreneurs needed to develop skills in ICT, leadership and self-leadership, team development and entrepreneurship skills. The entrepreneurship skills included skills on marketing and sales, brand development, productization, and business administration. Considering the three-year time the team entrepreneurs spent in the ICT-ESP, the challenges were remarkable. Changes to the organization of IT Bachelor education are also suggested in the study. At first, it should be admitted that the ICT-ESP produces IT Bachelors with a different set of competencies compared to the conventional way of educating IT Bachelors. Secondly, the number of courses on general topics in mathematics, physics, and languages for team entrepreneurs studying in the ICTESP should be reconsidered and the conventional course-based teaching of the topics should be reorganized to support the team coaching process of the team entrepreneurs with their practiceoriented projects. Third, the upcoming team entrepreneurs should be equipped with relevant information about the ICT-ESP and what it would require in practice to study as a team entrepreneur. Finally, the upcoming team entrepreneurs should be carefully selected before they start in the ICT-ESP to have a possibility to eliminate solo players and those who have a too romantic view of being a team entrepreneur. The results gained in the study provided answers to the original research questions and the objectives of the study were met. Even though the IT degree programme was terminated during the research process, the amount of qualitative data gathered made it possible to justify the interpretations done.

Abordagem de construção de arquitetura homogênea para comitês via meta-aprendizagem

In the world we are constantly performing everyday actions. Two of these actions are frequent and of great importance: classify (sort by classes) and take decision. When we encounter problems with a relatively high degree of complexity, we tend to seek other opinions, usually from people who have some knowledge or even to the extent possible, are experts in the problem domain in question in order to help us in the decision-making process. Both the classification process as the process of decision making, we are guided by consideration of the characteristics involved in the specific problem. The characterization of a set of objects is part of the decision making process in general. In Machine Learning this classification happens through a learning algorithm and the characterization is applied to databases. The classification algorithms can be employed individually or by machine committees. The choice of the best methods to be used in the construction of a committee is a very arduous task. In this work, it will be investigated meta-learning techniques in selecting the best configuration parameters of homogeneous committees for applications in various classification problems. These parameters are: the base classifier, the architecture and the size of this architecture. We investigated nine types of inductors candidates for based classifier, two methods of generation of architecture and nine medium-sized groups for architecture. Dimensionality reduction techniques have been applied to metabases looking for improvement. Five classifiers methods are investigated as meta-learners in the process of choosing the best parameters of a homogeneous committee.

A perceção dos alunos do 3º ciclo sobre a aprendizagem da Língua Portuguesa, da Matemática e da Educação Física

Para garantir a melhor aprendizagem para os alunos nas escolas, de forma a assegurar uma educação de qualidade e assim uma consequente preparação para o futuro dos mesmos na sociedade, é crucial que as escolas adotem estratégias de inclusão, de maneira a adaptar o ensino e a aprendizagem às diferentes características e necessidades dos alunos, e à diversidade que hoje em dia está patente no sistema de ensino. De forma a fomentar a educação inclusiva, é importante que as escolas e os professores organizem o processo ensino-aprendizagem com base na aprendizagem cooperativa, realizada em grupos heterogéneos, onde o sucesso do grupo depende de todos os alunos que o constituem. As interações estabelecidas tanto no seio do grupo como também entre o professor e o grupo de alunos, são fundamentais, pois são consideradas um fator positivo que assegura uma aprendizagem de qualidade sustentada nas relações de interdependência existentes entre alunos e professores. O objetivo deste estudo é perceber qual a perceção dos alunos do 3º ciclo relativamente à aprendizagem na sala de aula nas disciplinas de Língua Portuguesa, Matemática e Educação Física e se essas perceções variam de forma positiva ou negativa em função da disciplina em causa. A amostra foi constituída por 1159 alunos do 3º ciclo, à qual foi aplicado o questionário “A perceção dos alunos sobre a aprendizagem na sala de aula” (A.S.A - P.A., Leitão, 2012). Concluiu-se que os alunos têm uma perceção mais positiva sobre as aprendizagens na disciplina de Educação Física quando comparada com as demais. A perceção dos alunos do 3º ciclo relativamente à interdependência aluno/aluno varia em função da disciplina. Relativamente à interdependência professor/aluno, a perceção dos alunos só não varia entre as disciplinas de Língua Portuguesa e Educação Física quando comparadas. Já em relação à negociação, a perceção dos alunos apenas varia entre as disciplinas de Língua Portuguesa e Matemática quando comparadas. Por fim, referente à meta-aprendizagem concluímos que a perceção dos alunos do 3º ciclo não varia em função da disciplina.

Práticas de aprendizagem cooperativa no Ensino básico e secundário : perceção de professores de educação física

A realização deste estudo tem como objetivo analisar a perceção de professores de Educação Física relativamente a cinco indicadores de qualidade do processo educativo numa ótica inclusiva: Interdependência Aluno-Aluno; Interdependência Professor-Aluno; Negociação; Meta-Aprendizagem e Interdependência Professor-Professor (Variáveis Dependentes). Por outro lado esta investigação tem ainda como objetivo verificar se existem diferenças significativas na perceção dos professores de acordo com o nível de ensino. Para o efeito, numa primeira fase foi realizada uma revisão da literatura tendo como foco a nova realidade onde se insere atualmente a escola, assim como as relações de cooperação que se estabelecem dentro da mesma em contexto de sala de aula, tendo em vista a inclusão de todos os alunos. Posteriormente foi efetuado um estudo observacional de carater quantitativo, utilizando medidas numéricas para testar as hipóteses, em vinte e quatro escolas do grande Porto e Lisboa, através da distribuição do questionário (ASA-PPP, Leitão, 2012). A amostra é constituída por cento e cinquenta e oito professores de Educação Física distribuídos por três níveis de ensino: 1º ciclo, 2º / 3º ciclo e Secundário. O procedimento estatístico utilizado para a interpretação dos resultados é o teste de comparação de médias ANOVA, através do “EzAnalyse 3.0”. Os resultados deste estudo confirmam três das cinco hipóteses levantadas, indicando a existência de diferenças significativas relativamente à perceção dos professores na Interdependência Professor-Aluno, Negociação e Meta-Aprendizagem em função do nível de ensino. Por outro lado, verificou-se ainda que relativamente à Interdependência Aluno-Aluno e Interdependência Professor-Professor, não se constataram diferenças significativas em função do nível de ensino, tendo sido então consideradas nulas estas duas hipóteses. Por outro lado foi possível constatar para esta amostra que a perceção dos professores de Educação Física em exercício de funções no primeiro ciclo, face a estas práticas de ensino, são significativamente superiores, diminuindo progressivamente ao longo da evolução dos ciclos.

Rank Aggregation for Pattern Classifier Selection in Remote Sensing Images

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

A educação como arte do fluir: Paregogia e Aikido

Pós-graduação em Artes - IA

Novas abordagens para configurações automáticas dos parâmetros de controle em comitês de classificadores

Significant advances have emerged in research related to the topic of Classifier Committees. The models that receive the most attention in the literature are those of the static nature, also known as ensembles. The algorithms that are part of this class, we highlight the methods that using techniques of resampling of the training data: Bagging, Boosting and Multiboosting. The choice of the architecture and base components to be recruited is not a trivial task and has motivated new proposals in an attempt to build such models automatically, and many of them are based on optimization methods. Many of these contributions have not shown satisfactory results when applied to more complex problems with different nature. In contrast, the thesis presented here, proposes three new hybrid approaches for automatic construction for ensembles: Increment of Diversity, Adaptive-fitness Function and Meta-learning for the development of systems for automatic configuration of parameters for models of ensemble. In the first one approach, we propose a solution that combines different diversity techniques in a single conceptual framework, in attempt to achieve higher levels of diversity in ensembles, and with it, the better the performance of such systems. In the second one approach, using a genetic algorithm for automatic design of ensembles. The contribution is to combine the techniques of filter and wrapper adaptively to evolve a better distribution of the feature space to be presented for the components of ensemble. Finally, the last one approach, which proposes new techniques for recommendation of architecture and based components on ensemble, by techniques of traditional meta-learning and multi-label meta-learning. In general, the results are encouraging and corroborate with the thesis that hybrid tools are a powerful solution in building effective ensembles for pattern classification problems.

Technologies for urban and rural internet of things

Nowadays, application domains such as smart cities, agriculture or intelligent transportation, require communication technologies that combine long transmission ranges and energy efficiency to fulfill a set of capabilities and constraints to rely on. In addition, in recent years, the interest in Unmanned Aerial Vehicles (UAVs) providing wireless connectivity in such scenarios is substantially increased thanks to their flexible deployment. The first chapters of this thesis deal with LoRaWAN and Narrowband-IoT (NB-IoT), which recent trends identify as the most promising Low Power Wide Area Networks technologies. While LoRaWAN is an open protocol that has gained a lot of interest thanks to its simplicity and energy efficiency, NB-IoT has been introduced from 3GPP as a radio access technology for massive machine-type communications inheriting legacy LTE characteristics. This thesis offers an overview of the two, comparing them in terms of selected performance indicators. In particular, LoRaWAN technology is assessed both via simulations and experiments, considering different network architectures and solutions to improve its performance (e.g., a new Adaptive Data Rate algorithm). NB-IoT is then introduced to identify which technology is more suitable depending on the application considered. The second part of the thesis introduces the use of UAVs as flying Base Stations, denoted as Unmanned Aerial Base Stations, (UABSs), which are considered as one of the key pillars of 6G to offer service for a number of applications. To this end, the performance of an NB-IoT network are assessed considering a UABS following predefined trajectories. Then, machine learning algorithms based on reinforcement learning and meta-learning are considered to optimize the trajectory as well as the radio resource management techniques the UABS may rely on in order to provide service considering both static (IoT sensors) and dynamic (vehicles) users. Finally, some experimental projects based on the technologies mentioned so far are presented.

Multi-apprentice learning for meta-heuristics parameter tuning in a multi agent scheduling system

The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed in order to solve the parameter tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.

Meta-morphs: – being a mediator of the ‘thin’ place and ‘thin’ experience in the HE classroom. A paper on the experience of creating transformations in HE teaching and learning

The research which underpins this paper began as a doctoral project exploring archaic beliefs concerning Otherworlds and Thin Places in two particular landscapes - the West Coast of Wales and the West Coast of Ireland. A Thin Place is an ancient Celtic Christian term used to describe a marginal, liminal realm, beyond everyday human experience and perception, where mortals could pass into the Otherworld more readily, or make contact with those in the Otherworld more willingly. To encounter a Thin Place in ancient folklore was significant because it engendered a state of alertness, an awakening to what the theologian John O’ Donohue (2004: 49) called “the primal affection.” These complex notions and terms will be further explored in this paper in relation to Education. Thin Teaching is a pedagogical approach which offers students the space to ruminate on the possibility that their existence can be more and can mean more than the categories they believed they belonged to or felt they should inhabit. Central to the argument then, is that certain places and their inhabitants can become revitalised by sensitively considered teaching methodologies. This raises interesting questions about the role spirituality plays in teaching practice as a tool for healing in the twenty first century.

The effectiveness of workplace coaching:a meta-analysis of learning and performance outcomes from coaching

This study presents a meta-analysis synthesizing the existing research on the effectiveness of workplace coaching. We exclusively explore workplace coaching provided by internal or external coaches and therefore exclude cases of manager-subordinate and peer coaching. We propose a framework of potential outcomes from coaching in organizations, which we examine meta-analytically (k = 17). Our analyses indicated that coaching had positive effects on organizational outcomes overall (δ = 0.36), and on specific forms of outcome criteria (skill-based δ = 0.28; affective δ = 0.51; individual-level results δ = 1.24). We also examined moderation by a number of coaching practice factors (use of multisource feedback; type of coach; coaching format; longevity of coaching). Our analyses of practice moderators indicated a significant moderation of effect size for type of coach (with effects being stronger for internal coaches compared to external coaches) and use of multisource feedback (with the use of multisource feedback resulting in smaller positive effects). We found no moderation of effect size by coaching format (comparing face-to-face, with blended face-to-face and e-coaching) or duration of coaching (number of sessions or longevity of intervention). The effect sizes give support to the potential utility of coaching in organizations. Implications for coaching research and practice are discussed.

The effectiveness of workplace coaching:A meta-analysis of learning and performance outcomes; scale development; theoretical model of individual differences and longitudinal study

The extant literature on workplace coaching is characterised by a lack of theoretical and empirical understanding regarding the effectiveness of coaching as a learning and development tool; the types of outcomes one can expect from coaching; the tools that can be used to measure coaching outcomes; the underlying processes that explain why and how coaching works and the factors that may impact on coaching effectiveness. This thesis sought to address these substantial gaps in the literature with three linked studies. Firstly, a meta-analysis of workplace coaching effectiveness (k = 17), synthesizing the existing research was presented. A framework of coaching outcomes was developed and utilised to code the studies. Analysis indicated that coaching had positive effects on all outcomes. Next, the framework of outcomes was utilised as the deductive start-point to the development of the scale measuring perceived coaching effectiveness. Utilising a multi-stage approach (n = 201), the analysis indicated that perceived coaching effectiveness may be organised into a six factor structure: career clarity; team performance; work well-being; performance; planning and organizing and personal effectiveness and adaptability. The final study was a longitudinal field experiment to test a theoretical model of individual differences and coaching effectiveness developed in this thesis. An organizational sample of 84 employees each participated in a coaching intervention, completed self-report surveys, and had their job performance rated by peers, direct reports and supervisors (a total of 352 employees provided data on participant performance). The results demonstrate that compared to a control group, the coaching intervention generated a number of positive outcomes. The analysis indicated that coachees’ enthusiasm, intellect and orderliness influenced the impact of coaching on outcomes. Mediation analysis suggested that mastery goal orientation, performance goal orientation and approach motivation in the form of behavioural activation system (BAS) drive, were significant mediators between personality and outcomes. Overall, the findings of this thesis make an original contribution to the understanding of the types of outcomes that can be expected from coaching, and the magnitude of impact coaching has on outcomes. The thesis also provides a tool for reliably measuring coaching effectiveness and a theoretical model to understand the influence of coachee individual differences on coaching outcomes.

The impact of pre -training interventions on cognitive, skill, and affective learning outcomes: A meta -analytic examination of four decades of research

Technological advancements and the ever-evolving demands of a global marketplace may have changed the way in which training is designed, implemented, and even managed, but the ultimate goal of organizational training programs remains the same: to facilitate learning of a knowledge, skill, or other outcome that will yield improvement in employee performance on the job and within the organization (Colquitt, LePine, & Noe, 2000; Tannenbaum & Yukl, 1992). Studies of organizational training have suggested medium to large effect sizes for the impact of training on employee learning (e.g., Arthur, Bennett, Edens, & Bell, 2003; Burke & Day, 1986). However, learning may be differentially affected by such factors as the (1) level and type of preparation provided prior to training, (2) targeted learning outcome, (3) training methods employed, and (4) content and goals of training (e.g., Baldwin & Ford, 1988). A variety of pre-training interventions have been identified as having the potential to enhance learning from training and practice (Cannon-Bowers, Rhodenizer, Salas, & Bowers, 1998). Numerous individual studies have been conducted examining the impact of one or more of these pre-training interventions on learning. ^ I conducted a meta-analytic examination of the effect of these pre-training interventions on cognitive, skill, and affective learning. Results compiled from 359 independent studies (total N = 37,038) reveal consistent positive effects for the role of pre-training interventions in enhancing learning. In most cases, the provision of a pre-training intervention explained approximately 5–10% of the variance in learning, and in some cases, explained up to 40–50% of variance in learning. Overall attentional advice and meta-cognitive strategies (as compared with advance organizers, goal orientation, and preparatory information) seem to result in the most consistent learning gains. Discussion focuses on the most beneficial match between an intervention and the learning outcome of interest, the most effective format of these interventions, and the most appropriate circumstances under which these interventions should be utilized. Also highlighted are the implications of these results for practice, as well as propositions for important avenues for future research. ^