A Novelty Detection Approach to Seizure Analysis from Intracranial EEG Andrew B. Gardner 146 pages Directed by Dr. George Vachtsevanos and Dr. Brian Litt A framework for support vector machine classification of time series events is proposed and applied to analyze physiological signals recorded from epileptic patients. In contrast to previous works, this research formulates seizure analysis as a novelty detection problem which allows seizure detection and prediction to be treated uniformly, in a way that is capable of accommodating multichannel and/or multimodal measurements. Theoretical properties of the support vector machine algorithm employed provide a straightforward means for controlling the false alarm rate of the detector. The resulting novelty detection system was evaluated both offline and online on a corpus of 1077 hours of intracranial electroencephalogram (IEEG) recordings from 12 patients diagnosed with medically resistant temporal lobe epilepsy during evaluation for epilepsy surgery. These patients collectively had 118 seizures during the recording period. The performance of the novelty detection framework was assessed with an emphasis on four key metrics: (1) sensitivity (probability of correct detection), (2) mean detection latency, (3) early-detection fraction (prediction or detection of seizure prior to electrographic onset), and (4) false positive rate. Both the offline and online novelty detectors achieved state-of-the-art seizure detection performance. In particular, the online detector achieved 97.85% sensitivity, -13.3 second latency, and 40% early-detection fraction at an average of 1.74 false positive predictions per hour (Fph). These results demonstrate that a novelty detection approach is not only feasible for seizure analysis, but it improves upon the state-of-the-art as an effective, robust technique. Additionally, an extension of the basic novelty detection framework demonstrated its use as a simple, effective tool for examining the spread of seizure onsets. This may be useful for automatically identifying seizure focus channels in patients with focal epilepsies. It is anticipated that this research will aid in localizing seizure onsets, and provide more efficient algorithms for use in a real device.

The accuracy of load forecast has a significant impact for power companies on executing the plan of power development, reducing operating costs and providing reliable power to the client. Short-term load forecasting is to forecast load demand for the duration of one hour or less. This study presents a new approach to process load forecasting. A Support Vector Machine (SVM) was used for the initial load estimation. Particle Swarm Optimization (PSO) was then adopted to search for optimal parameters for the SVM. In doing the load forecast, training data is the most important factor to affect the calculation time. Using more data for model training should provide a better forecast results, but it needs more computing time and is less efficient. Applications of data mining can provide means to reduce the data requirement and the computing time. The proposed Modified Support Vector Machines approach can be proved to provide a more accurate load forecasting.

Essential proteins affect the cellular life deeply, but it is hard to identify them. Protein-protein interaction is one of the ways to disclose whether a protein is essential or not. We notice that many researchers use the feature set composed of topology properties from protein-protein interaction to predict the essential proteins. However, the functionality of a protein is also a clue to determine its essentiality. In this thesis, to build SVM models for predicting the essential proteins, our feature set contains the sequence properties which can influence the protein function, topology properties and protein properties. In our experiments, we download Scere20070107, which contains 4873 proteins and 17166 interactions, from DIP database. The ratio of essential proteins to nonessential proteins is nearly 1:4, so it is imbalanced. In the imbalanced dataset, the best values of F-measure, MCC, AIC and BIC of our models are 0.5197, 0.4671, 0.2428 and 0.2543, respectively. We build another balanced dataset with ratio 1:1. For balanced dataset, the best values of F-measure, MCC, AIC and BIC of our models are 0.7742, 0.5484, 0.3603 and 0.3828, respectively. Our results are superior to all previous results with various measurements.

In this thesis, a popular music genre recognition approach for Japanese popular music using SVM (support vector machine) with forward feature selection is proposed. First, various common acoustic features are extracted from the digital signal of popular music songs, including sub-bands, energy, rhythm, tempo, formants. A set of the most appropriate features for the genre identification is then selected by the proposed forward feature selection technique. Experiments conducted on the database consisting of 296 Japanese popular music songs demonstrate that the accuracy of recognition the proposed algorithm can achieve approximately 78.81% and the accuracy is stable when the number of testing music songs is increased.

In this thesis, we present a vehicle detection and tracking system based on image processing and pattern recognition of single camera image sequences. Both software design and hardware implementation are considered. In the hypothesis generation (HG) step and the hypothesis verification (HV) step, we use the shadow detection technique combined with the proposed constrained vehicle width/distance ratio to eliminate unreasonable hypotheses. Furthermore, we use SVM classifier, a popular machine learning technique, to verify the generated hypothesis more precisely. In the vehicle tracking step, we limit vehicle tracking duration and periodic vehicle detection mechanisms. These tracking methods alleviate our driver-assistant system from executing complex operations of vehicle detection repeatedly and thus increase system performance without sacrificing too much in case of tracking wrong objects. Based on the the profiling of the software execution time, we implement by hardware the most critical part, the preprocessing of intensity conversion and edge detection. The complete software/hardware embedded system is realized in a FPGA prototype board, so that performance of whole system could achieve real-time processing without too much hardware cost.

The disulfide bond in a protein is a single covalent bond formed from the oxidation of two cysteines. It plays an important role in the folding and structure stability, and may regulate protein functions. The connectivity prediction problem is difficult because the number of possible patterns grows rapidly with respect to the number of cysteines. We discover some rules to discriminate the patterns with high accuracy in many methods. We implement multiple SVM methods, and utilize the BKS to fuse these classifiers. We apply the hybrid method to SP39 dataset with 4-fold cross-validation for the comparison with the previous works. We raise the accuracy to 71.5%, which improves significantly that of the best previous work, 65.9%.

From the large scale of documents effective to find the near-duplicate document, has been a very important issue. In this paper, we propose a new method to detect near-duplicate document from the large scale dataset, our method is divided into three parts, feature selection, similarity measure and discriminant derivation. In feature selection, document will be detected after preprocessed. Documents have to remove signals, stop words ... and so on. We measure the value of the term weight in the sentence, and then choose the terms which have higher weight in the sentence. These terms collected as a feature of the document. The document¡¦s feature set collected by these features. Similarity measure is based on similarity function to measure the similarity value between two feature sets. Discriminant derivation is based on support vector machine which train a classifiers to identify whether a document is a near-duplicate or not. support vector machine is a supervised learning strategy. It trains a classifier by the training patterns. In the characteristics of documents, the sentence-level features are more effective than terms-level features. Besides, learning a discriminant by SVM can avoid trial-and-error efforts required in conventional methods. Trial-and-error is going to find a threshold, a discriminant value to define document¡¦s relation. In the final analysis of experiment, our method is effective in near-duplicate document detection than other methods.

The selection and modification of kernel functions is a very important problem in the field of support vector learning. However, the kernel function of a support vector machine has great influence on its performance. The kernel function projects the dataset from the original data space into the feature space, and therefore the problems which couldn¡¦t be done in low dimensions could be done in a higher dimension through the transform of the kernel function. In this thesis, we adopt the FCM clustering algorithm to group data patterns into clusters, and then use a statistical approach to calculate the standard deviation of each pattern with respect to the other patterns in the same cluster. Therefore we can make a proper estimation on the distribution of data patterns and assign a proper standard deviation for each pattern. The standard deviation is the same as the variance of a radial basis function. Then we have the origin data patterns and the variance of each data pattern for support vector learning. Experimental results have shown that our approach can derive better kernel functions than other methods, and also can have better learning and generalization abilities.

This study addresses sorting problems with predefined ordinal classes. We develop a new method based on Support Vector Machine (SVM) model, which is mainly used for nominal binary or multi-class classification processes. In the proposed method, the SVM model is extended to include the preferences of the decision maker and the ordinal relationship between classes in sorting problems. New sets of constraints are added to the SVM model. We demonstrate the performance of the proposed method through several data sets. We compare the results with those of classical SVM model and UTADIS method, a well-known multiple criteria sorting method. We also analyze the effect of feature space mapping by Kernel Trick utilization on the results.

Functional data refer to data which consist of observed functions or curves evaluated at a finite subset of some interval. In this dissertation, we discuss statistical analysis, especially classification and regression when data are available in function forms. Due to the nature of functional data, one considers function spaces in presenting such type of data, and each functional observation is viewed as a realization generated by a random mechanism in the spaces. The classification procedure in this dissertation is based on dimension reduction techniques of the spaces. One commonly used method is Functional Principal Component Analysis (Functional PCA) in which eigen decomposition of the covariance function is employed to find the highest variability along which the data have in the function space. The reduced space of functions spanned by a few eigenfunctions are thought of as a space where most of the features of the functional data are contained. We also propose a functional regression model for scalar responses. Infinite dimensionality of the spaces for a predictor causes many problems, and one such problem is that there are infinitely many solutions. The space of the parameter function is restricted to Sobolev-Hilbert spaces and the loss function, so called, e-insensitive loss function is utilized. As a robust technique of function estimation, we present a way to find a function that has at most e deviation from the observed values and at the same time is as smooth as possible.