Support vector machines (SVMs) are a powerful tool for classification problems. SVMs have only been developed in the last 20 years with the availability of cheap and abundant computing power. SVMs are a non-statistical approach and make no assumptions about the distribution of the data. Here support vector machines are applied to a classic data set from the machine learning literature and the out-of-sample misclassification rates are compared to other classification methods. Finally, an algorithm for using support vector machines to address the difficulty in imputing missing categorical data is proposed and its performance is demonstrated under three different scenarios using data from the 1997 National Labor Survey.

A network gateway is a mechanism which provides protocol translation and/or validation of network traffic using the metadata contained in network packets. For media applications such as Voice-over-IP, the portion of the packets containing speech data cannot be verified and can provide a means of maliciously transporting code or sensitive data undetected. One solution to this problem is through Voice Activity Detection (VAD). Many VAD’s rely on time-domain features and simple thresholds for efficient speech detection however this doesn’t say much about the signal being passed. More sophisticated methods employ machine learning algorithms, but train on specific noises intended for a target environment. Validating speech under a variety of unknown conditions must be possible; as well as differentiating between speech and nonspeech data embedded within the packets. A real-time speech detection method is proposed that relies only on a clean speech model for detection. Through the use of Gammatone filter bank processing, the Cepstrum and several frequency domain features are used to train a One-Class Support Vector Machine which provides a clean-speech model irrespective of environmental noise. A Wiener filter is used to provide improved operation for harsh noise environments. Greater than 90% detection accuracy is achieved for clean speech with approximately 70% accuracy for SNR as low as 5dB

Nuclear Power Plants (NPP) undergo fault and sensitivity analysis with scenario modelling to predict catastrophic events, specifically releases of Cesium 137 (Cs-137). The purpose of this thesis is to find which of 108 input-features from Modular Accident Analysis Program (MAAP)simulation code are important, when there is large release of Cs-137 emissions. The features are tested all together and in their groupings. To find important features, the Machine learning (ML) model Random Forest (RF) has a built-in attribute which identifies important features. The results of RF model classification are corroborated with Support Vector Machines (SVM), K-Nearest Neighbor (KNN) and use k-folds cross validation to improve and validate the results, resulting in a near 90% accuracy for the three ML models. RF is successful at identifying important features related to Cs-137 emissions, by using the classification model to first identify top features, to further train the models at identifying important input-features. The discovered input-features are important both within their individual groups, but also when including all features simultaneously. The large number of features included did not disrupt RF much, but the skewed dataset with few classified extreme events caused the accuracy to be lower at near 90%.

The option expiration effect is a well-studied phenome, however, few studies have implemented machine learning models to predict the effect on the underlying stock market due to options expiration. In this paper four machine learning models, SVM, random forest, AdaBoost, and LSTM, are evaluated on their ability to predict whether the underlying index rises or not on the day of option expiration. The options expiration effect is mainly driven by portfolio rebalancing made by market makers who aim to maintain delta-neutral portfolios. Whether or not market makers need to rebalance their portfolios depend on at least two variables; gamma and open interest. Hence, the machine learning models in this study use gamma exposure (i.e. a combination of gamma and open interest) to predict the options expiration effect. Furthermore, four architectures of LSTM are implemented and evaluated. The study shows that a three-layered many-to-one LSTM model achieves superior results with an F1 score of 62%. However, none of the models achieved better predictions than a model that predicts only positive classes. Some of the problems regarding gamma exposure are discussed and possible improvements for future studies are given. / Flera studier har visat att optionsmarknaden påverkar aktiemarknaden, speciellt vid optioners utgångsdatum. Dock har få studier undersökt maskininlärningsmodellers förmåga att förutse denna effekt. I den här studien, implementeras och utvärderas fyra olika maskininlärningsmodeller, SVM, random forest, AdaBoost, och LSTM, med syftet att förutse om den underliggande aktiemarknaden stiger vid optioners utgångsdatum. Att optionsmarknaden påverkar aktiemarknaden vid optioners utgångsdatum beror på att market makers ombalanserar sina portföljer för att bibehålla en delta-neutral portfölj. Market makers behov av att ombalansera sina portföljer beror på åtminstone två variabler; gamma och antalet aktiva optionskontrakt. Därmed använder maskininlärningsmodellerna i denna studie GEX, som är en kombination av gamma och antalet aktiva optionskontrakt, med syftet att förutse om marknaden stiger vid optioners utgångsdatum. Vidare implementeras och utvärderas fyra olika varianter av LSTM modeller. Studien visar att en many-to-one LSTM modell med tre lager uppnådde bäst resultat med ett F1 score på 62%. Dock uppnådde ingen av modellerna bättre resultat än en modell som predicerar endast positiva klasser. Avslutningsvis diskuteras problematiken med att använda GEX och rekommendationer för framtida studier ges.

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