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Classification tasks are an integral part of science, industry, medicine, and business; being such a pervasive technique, its smallest improvement is valuable. Artificial Neural Network (ANN) is one of the strongest techniques used in many disciplines for classification. The ANN technique suffers from drawbacks such as intransparency in spite of its high prediction power. In this dissertation, motivated by learning styles in human brains, ANN’s shortcomings are assuaged and its learning power is improved. Self-Organizing Map (SOM), an ANN variation which has strong unsupervised power, and Feedforward ANN, traditionally used for classification tasks, are hybridized to solidify their benefits and help remove their limitations. These benefits are in two directions: enhancing ANN’s learning power, and improving decision-making. First, the proposed method, named Self-Organizing Error-Driven (SOED) Artificial Neural Network (ANN), shows significant improvements in comparison with usual ANNs. We show SOED is a more accurate, more reliable, and more transparent technique through experimentation with five famous benchmark datasets. Second, the hybridization creates space for inclusion of decision-making goals at the level of ANN’s learning. This gives the classifier the opportunity to handle the inconclusiveness of the data smarter and in the direction of decision-making goals. Through three case studies, naming 1) churn decision analytics, 2) breast cancer diagnosis, and 3) quality control decision making through thermal monitoring of additive manufacturing processes, this novel and cost-sensitive aspect of SOED has been explored and lead to much quantified improvement in decision-making.

Uninterrupted power supply is an important requirement for electric ship since it has to confront frequent travel and hostilities. However, the occurrence of faults in the shipboard power systems interrupts the power service continuity and leads to the severe damage on the electrical equipments. Faults need to be quickly detected and isolated in order to restore the power supply and prevent the massive cascading outage effect on the electrical equipments. This thesis presents an Artificial Neural Network (ANN) based method for the fault classification and location in MVDC shipboard power systems using the transient information in the fault voltage and current waveforms. The proposed approach is applied to the cable of an equivalent MVDC system which is simulated using PSCAD. The proposed method is efficient in detecting the type and location of DC cable faults and is not influenced by changes in electrical parameters like fault resistance and load.

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Computational creativity is a new and developing field of artificial intelligence concerned with computational systems that either autonomously produce original and functional products, or that augment the ability of humans to do so. As the role of computers in our daily lives is continuing to expand, the need for such systems is becoming increasingly important. We introduce and document the development of a new “creative” system, called DARCI (Digital ARtist Communicating Intention), that is designed to autonomously create novel artistic images that convey linguistic concepts to the viewer. Within the scope of this work, the system becomes capable of creating non-photorealistic renderings of existing image compositions so that they convey the semantics of given adjectives. Ultimately, we show that DARCI is capable of producing surprising artifacts that are competitive, in some ways, with those produced by human artists. As with the development of any “creative” system, we are faced with the challenges of incorporating the philosophies of creativity into the design of the system, assessing the system's creativity, overcoming technical shortcomings of extant modern algorithms, and justifying the system within its creative domain (in this case, visual art). In meeting these challenges with DARCI, we demonstrate three broad contributions of the system: 1) the contribution to the field of computational creativity in the form of an original system, new approaches to achieving autonomy in creative systems, and new practical assessment methods; 2) the contribution to the field of computer vision in the form of new image features for affective image annotation and a new dataset; and 3) the contribution to the domain of visual art in the form of mutually beneficial collaborations and participation in several art galleries and exhibits.

Dynamic pricing is a difficult problem for machine learning. The environment is noisy, dynamic and has a measurable cost associated with exploration that necessitates that learning be done in short-time horizons. These short-time horizons force the learning algorithms to make pricing decisions based on scarce data. In this work, various machine learning algorithms are compared in the context of dynamic pricing. These algorithms include the Kalman filter, artificial neural networks, particle swarm optimization and genetic algorithms. The majority of these algorithms have been modified to handle the pricing problem. The results show that these adaptations allow the learning algorithms to handle the noisy dynamic conditions and to learn quickly.

The following work presents a new set of general methods for improving neural network accuracy on classification tasks, grouped under the label of classification-based methods. The central theme of these approaches is to provide problem representations and error functions that more directly improve classification accuracy than conventional learning and error functions. The CB1 algorithm attempts to maximize classification accuracy by selectively backpropagating error only on misclassified training patterns. CB2 incorporates a sliding error threshold to the CB1 algorithm, interpolating between the behavior of CB1 and standard error backpropagation as training progresses in order to avoid prematurely saturated network weights. CB3 learns a confidence threshold for each combination of training pattern and output class. This models an error function based on the performance of the network as it trains in order to avoid local overfit and premature weight saturation. PL1 is a point-wise local binning algorithm used to calibrate a learning model to output more accurate posterior probabilities. This algorithm is used to improve the reliability of classification-based networks while retaining their higher degree of classification accuracy. These approaches are demonstrated to be robust to a variety of learning parameter settings and have better classification accuracy than standard approaches on a variety of applications, such as OCR and speech recognition.

User click modeling in web search is most commonly done through probabilistic graphical models. Due to the successful use of machine learning techniques in other fields of research, it is interesting to evaluate how machine learning can be applied to click modeling. In this thesis, modeling is done using recurrent neural networks trained on a distributed representation of the state of the art user browsing model (UBM). It is further evaluated how extending this representation with a set of latent variables that are easily derivable from click logs, can affect the model's prediction performance. Results show that a model using the original representation does not perform very well. However, the inclusion of simple variables can drastically increase the performance regarding the click prediction task. For which it manages to outperform the two chosen baseline models, which themselves are well performing already. It also leads to increased performance for the relevance prediction task, although the results are not as significant. It can be argued that the relevance prediction task is not a fair comparison to the baseline functions, due to them needing more significant amounts of data to learn the respective probabilities. However, it is favorable that the neural models manage to perform quite well using smaller amounts of data. It would be interesting to see how well such models would perform when trained on far greater data quantities than what was used in this project. Also tailoring the model for the use of LSTM, which supposedly could increase performance even more. Evaluating other representations than the one used would also be of interest, as this representation did not perform remarkably on its own. / Klickmodellering av användare i söksystem görs vanligtvis med hjälp av probabilistiska modeller. På grund av maskininlärningens framgångar inom andra områden är det intressant att undersöka hur dessa tekniker kan appliceras för klickmodellering. Detta examensarbete undersöker klickmodellering med hjälp av recurrent neural networks tränade på en distribuerad representation av en populär och välpresterande klickmodell benämnd user browsing model (UBM). Det undersöks vidare hur utökandet av denna representation med statistiska variabler som enkelt kan utvinnas från klickloggar, kan påverka denna modells prestanda. Resultaten visar att grundrepresentationen inte presterar särskilt bra. Däremot har användningen av simpla variabler visats medföra drastiska prestandaökningar när det kommer till att förutspå en användares klick. I detta syfte lyckas modellerna prestera bättre än de två baselinemodeller som valts, vilka redan är välpresterande för syftet. De har även lyckats förbättra modellernas förmåga att förutspå relevans, fastän skillnaderna inte är lika drastiska. Relevans utgör inte en lika jämn jämförelse gentemot baselinemodellerna, då dessa kräver mycket större datamängder för att nå verklig prestanda. Det är däremot fördelaktigt att de neurala modellerna når relativt god prestanda för datamängden som använts. Det vore intressant att undersöka hur dessa modeller skulle prestera när de tränas på mycket större datamängder än vad som använts i detta projekt. Även att skräddarsy modellerna för LSTM, vilket borde kunna öka prestandan ytterligare. Att evaluera andra representationer än den som användes i detta projekt är också av intresse, då den använda representationen inte presterade märkvärdigt i sin grundform.

Demand forecasting plays an important role for every business and gives companies an opportunity to prepare for coming shifts in the market. The empirical findings of this study aim to support construction equipment manufacturers, distributors, and suppliers in apprehending the equipment market in more depth and foreseeing market demand to be able to adjust their business strategies and production capacities, allocate resources more efficiently, optimize the level of output and stock and, as a result, reduce associated costs, increase profitability and competitiveness. It is demonstrated that demand for construction equipment is heavily influenced by changes in economic conditions and country-specific economic indicators can serve as reliable input parameters to anticipate fluctuations in the construction equipment market. The Artificial Neural Networks (ANN) forecasting technique has been successfully employed to predict sales of construction equipment four quarters ahead in selected countries (Germany, The United Kingdom, France, Italy, Norway, Russia, Turkey and Saudi Arabia) with country related economic indicators used as an input.

An ambitious long-term goal for neuroevolution, which studies how artificial evolutionary processes can be driven to produce brain-like structures, is to evolve neurocontrollers with a high density of neurons and connections that can adapt and learn from past experience. Yet while neuroevolution has produced successful results in a variety of domains, the scale of natural brains remains far beyond reach. In this dissertation two extensions to the recently introduced Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach are presented that are a step towards more brain-like artificial neural networks (ANNs). First, HyperNEAT is extended to evolve plastic ANNs that can learn from past experience. This new approach, called adaptive HyperNEAT, allows not only patterns of weights across the connectivity of an ANN to be generated by a function of its geometry, but also patterns of arbitrary local learning rules. Second, evolvable-substrate HyperNEAT (ES-HyperNEAT) is introduced, which relieves the user from deciding where the hidden nodes should be placed in a geometry that is potentially infinitely dense. This approach not only can evolve the location of every neuron in the network, but also can represent regions of varying density, which means resolution can increase holistically over evolution. The combined approach, adaptive ES-HyperNEAT, unifies for the first time in neuroevolution the abilities to indirectly encode connectivity through geometry, generate patterns of heterogeneous plasticity, and simultaneously encode the density and placement of nodes in space. The dissertation culminates in a major application domain that takes a step towards the general goal of adaptive neurocontrollers for legged locomotion.