Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 149 for “"Feature Space"”.
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Temporal Data Mining in a Dynamic Feature Space
… is characterized by changes to the underlying feature space. Seemingly little has been done to address this issue. This thesis presents FAE, an incremental ensemble approach to mining data subject to concept drift. FAE achieves better accuracies over four large datasets when compared with a …
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Restricting Supervised Learning: Feature Selection and Feature Space Partition
… to solve either because of the redundant features or because of the structural complexity of the generative function. Redundant features increase the learning noise and therefore decrease the prediction performance. Additionally, a number of problems in various applications such as …
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Classification performance of support vector machines on genomic data utilizing feature space selection techniques
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.
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A load identification and diagnostic framework for aggregate power monitoring
… degrading system performance, providing a rich feature space for fault detection and diagnostics (FDD). However, mainstream machine learning methods may overlook potential features with key physical context in the development of soft faults due to a lack of faulty load data in publicly available …
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Using information theoretic measures to evaluate support vector machine kernels
… kernel to successfully separate a class in some feature space. This method is built on the fundamental idea that kernel density estimation in some input space is equivalent to an inner product on some Hilbert space. Estimators of Renyi's generalized form of information theoretic measurements …
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A Machine Learning Approach to Recognize Environmental Features Associated with Social Factors
… census tracts, by extracting five environmental features from Google Street View (GSV) images. The five environmental features are garbage bags, greenery, and three distinct road damage types (longitudinal, transverse, and alligator cracks), which were identified using image classification, …
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Effective Features and Machine Learning Methods for Document Classification
… proposed, which aims to capture low-dimensional feature subset that facilitates improved performance in text classification. The experimental results have demonstrated the advantages and usefulness of the proposed method for text classification in high-dimensional feature space in terms of the …
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Visualizing object detection features
We introduce algorithms to visualize feature spaces used by object detectors. The tools in this paper allow a human to put on 'HOG goggles' and perceive the visual world as a HOG based object detector sees it. We found that these visualizations allow us to analyze object detection systems in new …
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Resolution Tricks and Disaggregation Tools for Smart Power Metering
… Thus, this work presents Adaptive NILM, a set of feature space selection and classification tools useful for nonintrusive load monitoring with limited training data when load operation drifts over time. These techniques are synthesized into a new NILM software package that allows for high-level …
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Classification of Acoustic Emission Signals from an Aluminum Pressure Vessel Using a Self-Organizing Map
… network clustered the data on a two-dimensional feature space according to the source of the signal. A total of 3,600 power spectra were used to train the neural network, and 1,800 were used to test the network. Initially there was overlap between the clusters on the two-dimensional feature …
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Neural Network Detection of Fatigue Crack Growth in Riveted Joints Using Acoustic Emission
… spectra were clustered onto a two-dimensional feature space using a Kohonen self organizing map (SOM). Then 132 crack growth and 137 rivet rubbing spectra were used to train a back-propagation neural network to provide automatic pattern classification. Although there was some overlap between …
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Image Classification using Gabor Filters and Machine Learning
Feature extraction and classification are important areas of research in image processing and computer vision with a myriad of applications in science and industry. The focus of this work is on the robust classification of tree and non-tree areas in aerial imagery of the eastern Andes mountains in …
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Deep Embedding Kernel
… both branches work through mapping data to a feature space that is supposedly more favorable towards the given task. This dissertation addresses the strengths and weaknesses of each mapping method through combining them and forming a family of novel deep architectures that center around the …
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Optimal feature selection and machine learning for high-level audio classification : a random forests approach
… is predominately dependent on the selected features. This thesis presents a detailed study to identify the suitable classification features and associate a suitable machine learning technique for the intended classification task. In particular, a systematic feature selection procedure is …
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Optimisation of Gaussian process regressions of molecular potential energy surfaces
… importance of the mathematical expression of the feature dimensions of the input space. Moreover, for Gaussian processes which are particularly popular in regression problems, the choice of an appropriate kernel function to construct the desired model is not straightforward. The feature spaces, …
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On Optimal Quantization and its Effect on Anomaly Detection and Image Classification
… density estimation to optimally quantize the feature space to generate a codebook used by a bag-of-features (BoF) image classifier. This thesis shows that the optimal smoothing calculation in density estimation can be used to systematically quantize the feature space to generate codebooks that …
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Simultaneous Behavior Onset Detection and Task Classification for Patients with Parkinson Disease Using Subthalamic Nucleus Local Field Potentials
… to the research of various properties and features of the STN LFP signals of several patients' behavior conditions. Features based on temporal and time-frequency analysis of the signals are developed and implemented. Evaluation and comparison of the features is conducted on several …
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Towards Robust Deep Neural Networks
… or invertible mapping function from the problemspace (such as software code inputs) to the feature space is hard. The study proposes an alternative; performing adversarial learning in the feature space and proving the projection of perturbed yet, valid malware, in the problem space into the …
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Knowledge-guided constructive induction
… how knowledge affects a domain's constructed feature space and instance space allows us to find better techniques more easily. Three case studies (bankruptcy, turfgrass management, and promoters) demonstrate how analysis of knowledge drives development of techniques. Although knowledge in each …
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