No / This chapter is concerned with the discussion of a computational framework to aid with gender classification in an automated fashion using the dynamics of a smile. The computational smile dynamics framework we discuss here uses the spatio-temporal changes on the face during a smile. Specifically, it uses a set of spatial and temporal features on the overall face. These include the changes in the area of the mouth, the geometric flow around facial features and a set of intrinsic features over the face. These features are explicitly derived from the dynamics of the smile. Based on it, a number of distinct dynamic smile parameters can be extracted which can then be fed to a machine learning algorithm for gender classification.

The nearest neighbour problem is of practical significance in a number of fields. Often we are interested in finding an object near to a given query object. The problem is old, and a large number of solutions have been proposed for it in the literature. However, it remains the case that even the most popular of the techniques proposed for its solution have not been compared against each other. Also, many techniques, including the old and popular ones, can be implemented in a number of ways, and often the different implementations of a technique have not been thoroughly compared either. This research presents a detailed investigation of different implementations of two popular nearest neighbour search data structures, KDTrees and Metric Trees, and compares the different implementations of each of the two structures against each other. The best implementations of these structures are then compared against each other and against two other techniques, Annulus Method and Cover Trees. Annulus Method is an old technique that was rediscovered during the research for this thesis. Cover Trees are one of the most novel and promising data structures for nearest neighbour search that have been proposed in the literature.

Nearest neighbour methods are a classical approach in nonparametric statistics. The k-nearest neighbour classifier can be traced back to the seminal work of Fix and Hodges (1951) and they also enjoy popularity in many other problems including density estimation and regression. In this thesis we study their use in three different situations, providing new theoretical results on the performance of commonly-used nearest neighbour methods and proposing new procedures that are shown to outperform these existing methods in certain settings. The first problem we discuss is that of entropy estimation. Many statistical procedures, including goodness-of-fit tests and methods for independent component analysis, rely critically on the estimation of the entropy of a distribution. In this chapter, we seek entropy estimators that are efficient and achieve the local asymptotic minimax lower bound with respect to squared error loss. To this end, we study weighted averages of the estimators originally proposed by Kozachenko and Leonenko (1987), based on the k-nearest neighbour distances of a sample. A careful choice of weights enables us to obtain an efficient estimator in arbitrary dimensions, given sufficient smoothness, while the original unweighted estimator is typically only efficient in up to three dimensions. A related topic of study is the estimation of the mutual information between two random vectors, and its application to testing for independence. We propose tests for the two different situations of the marginal distributions being known or unknown and analyse their performance. Finally, we study the classical k-nearest neighbour classifier of Fix and Hodges (1951) and provide a new asymptotic expansion for its excess risk. We also show that, in certain situations, a new modification of the classifier that allows k to vary with the location of the test point can provide improvements. This has applications to the field of semi-supervised learning, where, in addition to labelled training data, we also have access to a large sample of unlabelled data.

The simulation of a manufacturing process chain with the finite element method requires the selection of an appropriate finite element solver, element type and mesh density for each process of the chain. When the simulation results of one step are needed in a subsequent one, they have to be interpolated and transferred to another model. This paper presents an in-core grid index that can be created on a mesh represented by a list of nodes/elements. Finite element data can thus be transferred across different models in a process chain by mapping nodes or elements in indexed meshes. For each nodal or integration point of the target mesh, the index on the source mesh is searched for a specific node or element satisfying certain conditions, based on the mapping method. The underlying space of an indexed mesh is decomposed into a grid of variable-sized cells. The index allows local searches to be performed in a small subset of the cells, instead of linear searches in the entire mesh which are computationally expensive. This work focuses on the implementation and computational efficiency of indexing, searching and mapping. An experimental evaluation on medium-sized meshes suggests that the combination of index creation and mapping using the index is much faster than mapping through sequential searches.

Detecting when a title is put in an incorrect data category can be of interest for commercial digital services, such as streaming platforms, since they group movies by genre. Another example of a beneficiary is price comparison services, which categorises offers by their respective product. In order to find data points that are significantly different from the majority (outliers), outlier detection can be applied. A title in the wrong category is an example of an outlier. Outlier detection algorithms may require a metric that quantify nonsimilarity between two points. Text similarity functions can provide such a metric when comparing text data. The question therefore arises, "Which text similarity function is best suited for detecting incorrect titles in practical environments such as commercial digital services?" In this thesis, different text similarity functions are evaluated when set to detect outlying (incorrect) product titles, with both efficiency and effectiveness taken into consideration. Results show that the variance in performance between functions generally is small, with a few exceptions. The overall top performer is Sørensen-Dice, a function that divides the number of common words with the total amount of words found in both strings. While the function is efficient in the sense that it identifies most outliers in a practical time-frame, it is not likely to find all of them and is therefore deemed to not be effective enough to by applied in practical use. Therefore it might be better applied as part of a larger system, or in combination with manual analysis. / Att identifiera när en titel placeras i en felaktig datakategori kan vara av intresse för kommersiella digitala tjänster, såsom plattformar för filmströmning, eftersom filmer delas upp i genrer. Också prisjämförelsetjänster, som kategoriserar erbjudanden efter produkt skulle dra nytta. Outlier detection kan appliceras för att finna datapunkter som skiljer sig signifikant från de övriga (outliers). En titel i en felaktig kategori är ett exempel på en sådan outlier. Outlier detection algoritmer kan kräva ett mått som kvantifierar hur olika två datapunkter är. Text similarity functions kvantifierar skillnaden mellan textsträngar och kan därför integreras i dessa algoritmer. Med detta uppkommer en följdfråga: "Vilken text similarity function är bäst lämpad för att hitta avvikande titlar i praktiska miljöer såsom kommersiella digitala tjänster?”. I detta examensarbete kommer därför olika text similarity functions att jämföras när de används för att finna felaktiga produkttitlar. Jämförelsen tar hänsyn till både tidseffektivitet och korrekthet. Resultat visar att variationen i prestation mellan funktioner generellt är liten, med ett fåtal undantag. Den totalt sett högst presterande funktionen är Sørensen-Dice, vilken dividerar antalet gemensamma ord med det totala antalet ord i båda texttitlarna. Funktionen är effektiv då den identiferar de flesta outliers inom en praktisk tidsram, men kommer sannolikt inte hitta alla. Istället för att användas som en fullständig lösning, skulle det därför vara fördelaktigt att kombinera den med manuell analys eller en mer övergripande lösning.

The patterns of certain groups of urban places, selected on the basis of population size and area location, in southern Saskatchewan are classified by the use of the nearest neighbour technique. Through a study of the variations within the overall pattern, which are revealed by differences in the derived pattern statistic, a partial contribution is made to the understanding of the distributive process that underlies the observed settlement pattern. Explanations for the variations in the nature of the spatial arrangement of the various groups of places are suggested through the use of multivariate analysis, and by reference to theoretical and empirical works in the field of Central Place Theory. / Thesis / Master of Arts (MA)

This thesis deals with real logistic problem of the Heineken CZ Company. The company sets down an itinerary for each vehicle to distribute its goods to particular customers on daily basis. These itineraries are created manually, only with the skill of experienced driver. The goal of this thesis is to find a solution with an algorithm, which will be able to set optimal itineraries of all vehicles, so the total distance and therefore operating costs are minimized, with only the knowledge of distances between each two nodes.

The numerical model to forecast road conditions, Model of the Environment and Temperature of Roads (METRo), laid the foundation of solving the energy balance and calculating the temperature evolution of roads. METRo does this by providing a numerical modelling system making use of Road Weather Information Stations (RWIS) and meteorological projections. While METRo accommodates tools for correcting errors at each station, such as regional differences or microclimates, this thesis proposes machine learning as a supplement to the METRo prognostications for accounting station characteristics. Controlled experiments were conducted by comparing four regression algorithms, that is, recurrent and dense neural network, random forest and k-nearest neighbour, to predict the squared deviation of METRo forecasted road surface temperatures. The results presented reveal that the models utilising the random forest algorithm yielded the most reliable predictions of METRo deviations. However, the study also presents the promise of neural networks and the ability and possible advantage of seasonal adjustments that the networks could offer.

This paper uses statistical learning to examine and compare three different statistical methods with the aim to predict credit card fraud. The methods compared are Logistic Regression, K-Nearest Neighbour and Random Forest. They are applied and estimated on a data set consisting of nearly 300,000 credit card transactions to determine their performance using classification of fraud as the outcome variable. The three models all have different properties and advantages. The K-NN model preformed the best in this paper but has some disadvantages, since it does not explain the data but rather predict the outcome accurately. Random Forest explains the variables but performs less precise. The Logistic Regression model seems to be unfit for this specific data set.

<p>The ion current measured from the spark plug in a spark ignited combustion engine is used </p><p>as basis for analysis and control of the combustion variability caused by exhaust gas </p><p>recirculation. Methods for extraction of in-cylinder pressure information from the ion </p><p>current are analyzed in terms of reliability and processing efficiency. A model for the </p><p>recognition of combustion variability using this information is selected and tested on both </p><p>simulated and car data.</p>