نتایج جستجو برای: multiple classifiers fusion
تعداد نتایج: 889043 فیلتر نتایج به سال:
To resolve class-ambiguity in real world problems, we previously presented two different ensemble approaches with support vector machines (SVMs): multiple decision templates (MuDTs) and dynamic ordering of one-vs.-all SVMs (DO-SVMs). MuDTs is a classifier fusion method, which models intra-class variations as subclass templates. On the other hand, DO-SVMs is an ensemble method that dynamically s...
The aim of this work is to propose a new feature and score fusion based iris recognition approach where voting method on Multiple Classifier Selection technique has been applied. Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood r...
The Institute for Infocomm Research (IIR) team submitted two systems, namely the primary iir_primary_llr and the contrastive iir_contrast1_llr, to the 2011 NIST Language Recognition Evaluation (LRE). Both systems are based on the fusion of multiple classifiers. These classifiers are broadly divided into two groups: acoustic and phonotactic. Included in the submission are the result files: iir1/...
Image classification is one of the major data mining tasks in smart city applications. However, deploying models that have good generalization accuracy highly crucial for reliable decision-making such One ways to achieve through use multiple classifiers and fusion their decisions. This approach known as “decision fusion”. The requirement achieving results with decision there should be dissimila...
Our MED 13 system is an extension of our MED 12 system [12, 13], and consists of a collection of lowlevel and high-level features, feature-specific classifiers built upon those features, and a fusion system that combines features both through mid-level kernel fusion and late fusion. Our MED submissions include total of 24 different configurations which consist of combinations of 2 submission ti...
We present an extensive study on the performance of data agglomeration and decision-level fusion for robust cross-corpus emotion recognition. We compare joint training with multiple databases and late fusion of classifiers trained on single databases, employing six frequently used corpora of natural or elicited emotion, namely ABC, AVIC, DES, eNTERFACE, SAL, VAM, and three classifiers i. e. SVM...
Blind steganalysis is based on choice of the feature set and the machine learning classifiers used for classification. While the performance of individual classifiers is good, the classification accuracy is seen to increase by appropriate combination of classifiers. This research has implemented image steganalysis with fusion of classifiers by various data fusion schemes. We intend to analyse t...
− In Automatic Target Recognition (ATR) systems there are advantages to developing classifiers based on a portion of the signal. A partitioning technique is introduced in this paper that allows Rough Set Theory to be applied to real-world size problems. Rough Set Theory (RST) is an emerging concept for determining features and then classifiers from a training data set. RST guarantees that once ...
Hyperspectral image mostly have very large amounts of data which makes the computational cost and subsequent classification task a difficult issue. Firstly, to solve the problem of computational complexity, spectral clustering algorithm is imported to select efficient bands for subsequent classification task. Secondly, due to lack of labeled training sample points, this paper proposes a new alg...
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