In this paper, we present a search method with multi-class probability estimates for sketch-based 3D engineering part retrieval. The purpose of using probabilistic output from classification is to support high-quality part retrieval by motivating user relevance feedback from a ranked list of top categorical choices. Given a free-hand user sketch, we use an ensemble of classifiers to estimate th...

There is a trend in recent OCR development to improve system performance by combining recognition results of several complementary algorithms. This thesis examines the classi er combination problem under strict separation of the classi er and combinator design. None other than the fact that every classi er has the same input and output speci cation is assumed about the training, design or imple...

The construction of reliable machinery fault diagnostic systems is investigated in this thesis. The idea of using unitary sensorial information to develop automated fault classifiers is studied. The domain of fault diagnosis is used to investigate the concept of enforcing methodological diversity in the solutions with a view to obtaining robust and reliable systems by combining the decisions of...

We propose a novel unsupervised approach for distinguishing literal and non-literal use of idiomatic expressions. Our model combines an unsupervised and a supervised classifier. The former bases its decision on the cohesive structure of the context and labels training data for the latter, which can then take a larger feature space into account. We show that a combination of both classifiers lea...

The study of human language comprehension machine has become a important research topic around the world. Face recognition has great potential application value in economic, security, social security, crime, military and other fields, especially in the occasions where need verification or identification of user identity. This paper presents improved Eigen face method and method for identificati...

In this paper, we compare the performances of classifier combination methods (bagging, modified random subspace method, classifier selection, parametric fusion) to logistic regression in consideration of various characteristics of input data. Four factors used to simulate the logistic model are: (a) combination function among input variables, (b) correlation between input variables, (c) varianc...

Data Security has become a very critical part of any organizational information system. Intrusion Detection System (IDS) is used as a security measure to preserve data integrity and system availability from various attacks. This paper evaluates the performance of C4.5 classifier and its combination using bagging, boosting and stacking over NSLKDD dataset for IDS. This dataset set consists of se...

This paper presents an approach for optimizing both recognition and processing performance of a biometric system in identification mode. Multibiometric techniques facilitate bridging the gap between desired performance and current unimodal recognition rates. However, traditional parallel classifier combination techniques, such as Score sum, Borda count and Highest rank, cause further processing...

We introduce a novel tracking technique which uses dynamic confidence-based fusion of two different information sources for robust and efficient tracking of visual objects. Mean-shift tracking is a popular and well known method used in object tracking problems. Originally, the algorithm uses a similarity measure which is optimized by shifting a search area to the center of a generated “weight i...

Classifier performance is often enhanced through combining multiple streams of information. In the context of multistream HMM/ANN systems in ASR, a confidence measure widely used in classifier combination is the entropy of the posteriors distribution output from each ANN, which generally increases as classification becomes less reliable. The rule most commonly used is to select the ANN with the...