Discriminative features (resemblance)
Discriminative features in reversible stochastic attribute value grammars
... the features specific to flu- ency ranking (n-gram features) were selected as the most discriminative features in the reversible ...two features were uniform models from the perspec- ...
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Traffic sign detection based on simple XOR and discriminative features
... Traffic Sign Detection (TSD) is an important application in computer vision. It plays a crucial role in driver assistance systems, and provides drivers with safety and precaution information. In this paper, in addition ...
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Selection of Discriminative Features for Translation Texts
... most discriminative features that characterize the different Buddhist translation texts or other translation ...of features that can be extracted from translation texts and exploited the F-score and ...
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Emotion Recognition from Speech using Discriminative Features
... The choice of one-v/s-all SVM with linear, polynomial and radial basis function (RBF) kernels has been made. The performance of the SVM as a classifier of emotions has been scrutinized by observing for which of the ...
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Features Reduction using Wavelet and Discriminative Common Vector and Recognizing Faces using RBF
... most discriminative features for two reasons such as there is much redundant or irrelevant information contained in wavelet coefficients and it cannot recover new meaning underlying features which ...
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Modeling Discriminative-Based Features with Genetic Algorithm for Human Identity and Gender Recognit
... as features for human identity and gender recognition from gait ...C-AGI features of the same subject in such ...more discriminative modal-based features to improve the performance of human ...
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Spatial Filter Optimization Using Gaussian Kernel for Single Electro- Encephalo Gram (EEG) Trial Classification
... signal. Features derived from multiple channels result into a large sized feature vector but the available number of samples is ...and discriminative features is indispensable before classification ...
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EEG-Based Automatic Sleep Stage Classification
... of features. Then, the most discriminative features were selected and used to train a Support Vector Machines (SVM) [2,3,15,17] or a Bayesian classifier ...
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Unravelling Names of Fictional Characters
... that features in- trinsic to the names and without any reference to the plot or, in general, any other context are dis- ...most discriminative features are of phonolog- ical nature, rather than ...
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SDRNF: generating scalable and discriminative random nonlinear features from data
... discriminative features. However, as shown in many research proposals, discriminative features prove highly critical for learning an accurate classifier [12, ...
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Qualitative and quantitative spatio-temporal relations in daily living activity recognition
... and discriminative features from a set of feature templates that were designed based on qualitative and quanti- tative spatio-temporal feature representations (QQSTR) of the ...selected features are ...
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HSI Color Component Ratio for Compact Object Representation
... for discriminative features on segmented moving objects and these compact set of color feature for object representation tend to handle the large amount of local features in feature correspondence ...
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Face Spoof Detection Using Naive Bayes Classifier
... Real images behave normally whereas fake images are considered to exhibit abnormal behaviour. Using class modelling anomalies in the data are detected. Data which is labelled as anomaly considered as spoofed and does not ...
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Feature Extraction by Non Parametric Mutual Information Maximization (Kernel Machines Section)
... One well known linear transform for dimensionality reduction is principal component analysis or PCA (Devijver and Kittler, 1982). The transform is derived from eigenvectors corresponding to the largest eigenvalues of the ...
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Comparison of different feature sets for identification of variants in progressive aphasia
... the features, we remove each fea- ture set one at a time and measure the accuracy of the ...those features are not relevant to the classification (at least in combi- nation with the other ...
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A Discriminative Learning Model for Coordinate Conjunctions
... general discriminative learning model in which the score function is a linear function of the features as- signed to vertices and edges in the state space, and the weight of the features are ...
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Analysis of Patterns in Data Mining
... indexing discriminative substructures also. Discriminative substructures mean that if there are already some substructures in the index structure then new subgraphs are obtained and even they are frequent, ...
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Towards Accurate and Efficient Chinese Part of Speech Tagging
... the Features column means that the cur- rent configuration contains both the baseline features and new cluster-based features; the number is the total number of the clusters; the number in the #Sent ...
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Destination Prediction by Identifying and Clustering Prominent Features from Public Trajectory Datasets
... a discriminative method that chooses the most prominent features found in a public trajectory dataset, clusters the trajectories into groups based on these features, and performs destination ...
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Capturing Paradigmatic and Syntagmatic Lexical Relations: Towards Accurate Chinese Part of Speech Tagging
... the Features col- umn means current configuration contains both the baseline features and new cluster-based features; the number is the total number of the clusters; the sym- bol “+” in the Data ...
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