Towards Comprehensive Foundations of Computational Intelligence

Abstract. Although computational intelligence (CI) covers a vast variety of different methods it still lacks an integrative theory. Several proposals for CI foundations are discussed: computing and cognition as compression, meta-learning as search in the space of data models, (dis)similarity based methods providing a framework for such meta-learning, and a more general approach based on chains of transformations. Many useful transformations that extract information from features are discussed. Heterogeneous adaptive systems are presented as particular example of transformation-based systems, and the goal of learning is redefined to facilitate creation of simpler data models. The need to understand data structures leads to techniques for logical and prototype-based rule extraction, and to generation of multiple alternative models, while the need to increase predictive power of adaptive models leads to committees of competent models. Learning from partial observations is a natural extension towards reasoning based on perceptions, and an approach to intuitive solving of such problems is presented. Throughout the paper neurocognitive inspirations are frequently used and are especially important in modeling of the higher cognitive functions. Promising directions such as liquid and laminar computing are identified and many open problems presented.

Machine learning approaches for determining effective seeds for k -means algorithm

In this study, I investigate and conduct an experiment on two-stage clustering procedures, hybrid models in simulated environments where conditions such as collinearity problems and cluster structures are controlled, and in real-life problems where conditions are not controlled. The first hybrid model (NK) is an integration between a neural network (NN) and the k-means algorithm (KM) where NN screens seeds and passes them to KM. The second hybrid (GK) uses a genetic algorithm (GA) instead of the neural network. Both NN and GA used in this study are in their simplest-possible forms. In the simulated data sets, I investigate two properties: clustering performance comparisons and effects of five factors (scale, sample size, density, number of clusters, and number of variables) on the five clustering approaches (KM, NN, NK, GA, GK). Density, number of clusters, and dimension influence the clustering performance of all five approaches. KM, NK, and GK classify well when all clusters contain a similar number of observations, while NK and GK perform better than the KM. NN performs well when one cluster contains more observations than any other cluster. The two hybrid models perform at least as well as KM, although the environments are in favor of the KM. The most crucial information, the true number of clusters, is provided to the KM only. In addition, the cluster structures are simple: the clusters are well separated; the variances and cluster sizes are uniform; the correlation between any pair of variables and collinearity problems are not significant; and the observations are normally distributed. Real-life problems consist of three problems with a known natural cluster structure and one problem with an unknown natural cluster structure. Overall results indicate that GK performs better than KM, while NK is the worst performing among the five approaches. The two machine learning approaches generate better results than KM in an environment that does not favor KM. GK has shown to be the best or among the best in a simulated environment and in real-life situations. Furthermore, the GK can detect firms with promising financial prospect such as acquisition targets and firms with “buy” recommendation, better than all other approaches

New Fundamental Technologies in Data Mining

The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. The series of books entitled by "Data Mining" address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters. The contributing authors have highlighted many future research directions that will foster multi-disciplinary collaborations and hence will lead to significant development in the field of data mining

Performance modelling and analysis of olympic class sailing boats

PhD ThesisThe work in this thesis is preceded by a Master of Research in Marine Technology project between September 2004 and October 2005. The project was supervised by Professor Martin Downie and was carried out with significant time present in the field, working closely with Olympic sailors from multiple different classes. This project was funded by UK Sport and considered a pilot project to investigate the feasibility of using data logging equipment with GPS in the marine Olympic environment. A series of prototype systems were engineered to meet the requirements specified by the Royal Yachting Association. The engineering and validation of the software and hardware formed a key part of the project to ensure that the results obtained were accurate and repeatable. This included software design within two different software platforms as well as embedded hardware developments. Significant testing and development were implemented in the laboratory as well as on the water during the beginning of the project and as a continuous background task throughout the project. Over eighty days were spent in the field developing and testing hardware and software as well as determining the optimum performance analysis methods. Data loggers were fitted to several Olympic class boats during the evaluation process to ascertain the performance of the data logging system as well as the performance of the boat and crew. Data was logged from the onboard GPS and accelerometers and analysed post training. Later in the project, wind information was also collected and fused together with the onboard data post training. The hypothesis was to demonstrate performance gains in the participating classes through the means of quantitative analysis. Prior to the project the performance analysis had been almost entirely qualitative. Through the course of the project various techniques were developed allowing quantitative performance analysis to supplement the efforts of the training group and coach. Key performance factors were determined by data analysis techniques developed during the project. One of the significant tools developed was a tacking performance analysis routine which analysed multiple different styles of tacks, calculating the distance lost with respect to wind strength and course length resulting in an important strategic tool. Other tools relating to starting performance and straight line speed were also developed in custom software allowing rapid analysis of the data to feed back to the teams in the debrief

Affording expertise: integrating the biological, cultural and social sites of disciplinary skills and knowledge

The coherence of the concept of mental representations is increasingly in question, and hence accounts of expertise based on mental representation. I argue that such mental representational accounts are, at best, inadequate, and propose that turning to ecological psychology and affordance could provide the answer. However, there is no fully agreed understanding of affordance and so the thesis undertakes three main interrelated tasks: First, I review James J. Gibson's writings on affordance before setting out a revised account of affordance using Jacques Derrida's discussion of differance. Differance, as the generation of differences with the deferral of the meanings of those differences is adopted as a model for affordance. Second, affordance - as differance or difference and deferral - is taken as the minimal form of material agency. Drawing upon the process philosophy of Whitehead, agency is understood to be coextensive with material composition, and on this understanding an ontology of agency in medias res, considered as agency that develops within a pre-existing medium or milieu, is developed as an integrating framework within which biological, cultural and social phenomenon are combined in human agency in medias res. Third, human agency in medias res is explored through the process of acquiring expertise. As affordance is the primary ontology of all material reality. All human activity encompassing tools and instruments, representations and language is a concatenation of such constituents, hence expertise as the normative performance of disciplinary activities to disciplinary standards, is founded upon the proper concatenation of constituent affordance. Gaining expertise, meanwhile, precedes through the development of an ecological relation within activity that is founded upon specialised training and practice, and upon the social institution of someone who is socially legitimated as a master of their domain. By ecological relation, I mean to draw attention to the agency that develops and is sustained within the formation and maintenance of ritualised, instrumental, and discursive configurations that come to be identified as a particular domain of knowledge. The closely interrelated themes of affordance and agency in medias res are brought together in a case study of the development of expertise in archaeology by focusing on learning to identify (type) pottery, and on learning to excavate. In learning to type pottery, a novice is inculcated into the language-games of pottery. The formulation of typologies, meanwhile, shows how such language-games form, and how these language-games afford a semantic field that supports archaeologically mundane communications between archaeologists. The event of an excavation is used to focus on social dynamics seen from a perspective of agency in medias res and to demonstrate how wider social, economic and political influences intervene within archaeological discourse and practice to alter the agency of archaeologists in terms of the cognitive authority, and that of archaeology as discipline

Scientific Advances in STEM: From Professor to Students

This book collects the publications of the special Topic Scientific advances in STEM: from Professor to students. The aim is to contribute to the advancement of the Science and Engineering fields and their impact on the industrial sector, which requires a multidisciplinary approach. University generates and transmits knowledge to serve society. Social demands continuously evolve, mainly because of cultural, scientific, and technological development. Researchers must contextualize the subjects they investigate to their application to the local industry and community organizations, frequently using a multidisciplinary point of view, to enhance the progress in a wide variety of fields (aeronautics, automotive, biomedical, electrical and renewable energy, communications, environmental, electronic components, etc.). Most investigations in the fields of science and engineering require the work of multidisciplinary teams, representing a stockpile of research projects in different stages (final year projects, master’s or doctoral studies). In this context, this Topic offers a framework for integrating interdisciplinary research, drawing together experimental and theoretical contributions in a wide variety of fields

Application of ”ART SIMULATOR” for Manufacturing Similarity Identification in Group Technology Design - Chapter 10

This chapter 10 carried out the exceptional implementation of ART-1 neural network in the analysis of the manufacturing similarity of the cylindrical parts within the group technology design. Established concept of the group technology design begins from the complex part of the group or the group representative. Group representative has all the geometrical elements of the parts in group, and manufacturing procedure may be applied to the machining of any part in the group. The complex part may be realistic or a hypothetical one. The ART-1 artificial neural network provided manufacturing classification according to the geometrical similarities of work-pieces for the group of cylindrical parts. For the manufacturing similarity identification within the group technology design, software package "ART Simulator" is developed and presented in this chapter

Neuroverkkopohjaisen sijoitusstrategian hyödyntäminen indeksiosuusrahaston ennustamisessa

Tässä tutkimuksessa selvitetään, voiko koulutettua keinotekoista neuroverkkoa hyödyntää indeksiosuusrahaston ennustamisessa. Tutkimuksen teoriataustana käytetään tehokkaiden markkinoiden hypoteesia. Markkinoiden toimiessa tehokkaasti kaikki julkinen ja yrityksen arvon kannalta oleellinen uusi tieto heijastuu välittömästi ja täysimääräisesti arvopapereiden hintoihin. Tehokkailla markkinoilla yksittäinen sijoittaja ei voi saavuttaa säännöllisesti markkinoiden riskikorjattua tuottoa ylittävää ylituottoa. Tutkimuksen teoriaosuus perustuu rahoituksen taloustieteeseen ja menetelmäosuus koneoppimiseen. Tutkimusmenetelmänä käytetään keinotekoista neuroverkkoa, joka koulutetaan oppimaan rahoitusmarkkinoilta saatavien syötteiden avulla hinnan muodostumisen mekanismia. Koulutuksessa opittua mekanismia hyödynnetään seuraavan päivän indeksiosuusrahaston hintojen ennustamisessa. Oppimisalgoritmina käytetään Levenberg-Marquardt algoritmia. Algoritmin ennusteita muokataan erilaisten sijoitusstrategioiden avulla tarkempien ennusteiden saavuttamiseksi. Tämän tutkimuksen kohteena ovat pörssinoteeratun iShares Core S&P 500 -rahaston päätöskurssihinnat vuodesta 2005 vuoden 2015 loppuun. Aineistona käytetään S&P 500 -indeksistä johdettua historiallista aikasarja-aineistoa 1950-luvulta lähtien. Tutkimuksen mukaan neuroverkkoa voidaan hyödyntää iShares Core S&P 500 -indeksiosuusrahaston tuottojen suunnan ennustamisessa. Transaktiokustannusten ollessa alhaiset neuroverkosta johdetuilla ennusteilla saavutetaan hyviä tuottoja, muttei kuitenkaan markkinoiden riskikorjattua tuottoa ylittävää ylituottoa