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.
Results
Showing 1 to 11 of 11 for “"Nearest Neighbor Classifier"”.
-
A Direct Algorithm for the K-Nearest-Neighbor Classifier via Local Warping of the Distance Metric
The k-nearest neighbor (k-NN) pattern classifier is a simple yet effective learner. However, it has a few drawbacks, one of which is the large model size. There are a number of algorithms that are able to condense the model size of the k-NN classifier at the expense of accuracy. Boosting is …
-
Classification of Marine Vessels in a Littoral Environment Using a Novel Training Database
… available classification algorithm known as the Nearest Neighbor Classifier. The accuracy of the database as a training set is tested and recorded and potential improvements are documented. The second stage incorporates these identified improvements and reconfigures the database before retesting …
-
Stability of machine learning algorithms
… selecting the <em>most accurate and stable</em> classifier. The proposed classifier selection method introduces the statistical inference thinking into the machine learning society. Our selection method is shown to be consistent in the sense that the optimal classifier simultaneously achieves the …
-
Prosthesis control using a nearest neighbor electromyographic pattern classifier
A prosthesis control strategy using a nearest neighbor electromyographic pattern classifier was investigated with both a real time microprocessor-based controller and offline computational facilities. Four active electrodes for myoelectric signal amplitude detection were interfaced with a …
-
Identifying Application Protocols in Computer Networks Using Vertex Profiles
… features. The experimental results, using a nearest-neighbor classifier, show that this type of analysis can correctly classify the applications observed with greater than 80% accuracy.
-
Protein Fold Recognition Using Adaboost Learning Strategy
… information. In this thesis, we present a novel classifier on protein fold recognition, using AdaBoost algorithm that hybrids to k Nearest Neighbor classifier. The experiment framework consists of two tasks: (i) carry out cross validation within the training dataset, and (ii) test on unseen …
-
Landmine classification using possibilistic K-nearest neighbors with wideband electromagnetic induction data
… REQUEST.] In this thesis, a possibilistic K-nearest neighbor classifier is presented to distinguish between and classify mine and non-mine targets on data obtained from wideband electromagnetic induction sensors. The goal of this work is to develop methods for classifying wide-band …
-
Using Wireless Dry EEG System to Detect Mental Workload during Mental Arithmetic
… of the recorded EEG, was accomplished using a K-nearest neighbor classifier at an average accuracy of 91%. These findings validate the use of dry EEG as a valid technology that is capable of generating effective physiological measures for detecting mental workload levels.
-
New Approaches to Hierarchical Modeling — Frameworks, Algorithms, and Applications
… we were able to enhance the accuracy of the k-nearest neighbor classifier by removing minority class examples from clusters that were extracted from a supervised taxonomy; (3) to meta learning; we developed new algorithms that operate on supervised taxonomies and compute both the distribution …
-
Increasing the Precision of Forest Area Estimates through Improved Sampling for Nearest Neighbor Satellite Image Classification
… of three mosaicked Landsat ETM+ images with the nearest neighbor decision rule were explored. Large training data pools of single pixels were used in simulations to create samples with three sampling methods (random, stratified random, and systematic) and eight sample sizes (25, 50, 75, 100, 200, …
-
Classification of Dense Masses in Mammograms
… Pattern recognizing techniques such as nearest mean classifier and Support vector machine classifier are also used to classify the features. The initial stages include the processing of mammographic image to extract the relevant features that would be necessary for classification and …