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Showing 1 to 20 of 43 for “"Adaboost"”.
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Protein Fold Recognition Using Adaboost Learning Strategy
… 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 validation dataset, in which 90% of the proteins …
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Regularized Adaboost for RGBD video content identification
… boosting (SPB). Second, we propose a regularized Adaboost algorithm, which tackles SPB’s implicit assumption that video segments are statistically independent. Finally, we develop the first hybrid content ID system for synchronized RGB and depth (RGBD) videos. Experimental results show the …
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The Influence of Scoring Parameters on Precision-Based AdaBoost
… of the individual classifiers' predictions. AdaBoost, a popular ensemble-based approach, combines the votes of classifiers by assigning weights to their predictions based on the classifiers' overall accuracy. A modifica- tion of AdaBoost's weighting scheme is precision-based AdaBoost, which …
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Topics in imbalanced data classification : AdaBoost and Bayesian relevance vector machine
… first part is to study the Adaptive Boosting (AdaBoost) algorithm. AdaBoost is an effective solution for classification, but it still needs improvement in the imbalanced data problem. This part proposes a method to improve the AdaBoost algorithm using the new weighted vote parameters for the …
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Fitting AdaBoost Models From Imbalanced Data with Applications in College Basketball
… to use with Adaptive Boosting (AdaBoost). Based on a simulation study, we’ve found that combining AdaBoost with various sampling techniques provides an increased weighted accuracy across classes for progressively larger data imbalances. The three Synthetic Minority Oversampling …
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Speeding up Adaboost object detection with motion segmentation and Haar feature acceleration
… by Viola et. al. This algorithm is based on Adaboost and uses Haar features to detect objects. The main reason for its popularity is very low false positive rates and the fact that the classifier network can be trained for any detection task. The use of Haar basis functions to represent key …
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Enhancing Telecom Churn Prediction: Adaboost with Oversampling and Recursive Feature Elimination Approach
… Machine, Decision Tree, Random Forest, and AdaBoost.</p> <p>The study is segmented into three sets of experiments, each focusing on a different approach to building the churn prediction model. The model is constructed using the original training set in the first set of experiments. The …
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Investigating ecosystem-level effects of gillnet bycatch in Lake Erie: implications for commercial fisheries management
… be observed in the west basin of Lake Erie. The AdaBoost algorithm was applied in conjunction with the generalized linear/additive models to analyze catch rates of walleye, yellow perch and white perch. Three- and five-fold cross-validations were conducted to evaluate the performance of each …
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Integrating Digital Aerial Photography and Lidar-Derived Information For Object-Based Coastal Tidal Marsh Classification Using Tree-Based Ensemble Algorithms
… algorithms (i.e. Random Forest and Adaboost tree). Optimal scales were analyzed for each scheme and classification results from all eight schemes were compared. Interestingly, classification results from all eight schemes did not exhibit significant difference with segmentation …
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Análisis de la Encuesta de Salud Nacional y Examen de Nutrición de Estados Unidos (NHANES) usando machine learning
… Classification, Gradient Boosting Classifier, AdaBoost Classifier, Random Forest Classifier, Naive Bayes, Logistic Regression y k-NN de la librería sklearn. El mejor modelo se obtiene con AdaBoost y una exactitud de 76.33, aunque el Naive Bayes ofrece un buen resultado del TPR de 62.69 al …
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Learning compact hashing codes for large-scale similarity search
… investigate two classes of approach: regularized Adaboost and signal-to-noise ratio (SNR) maximization. The regularized Adaboost builds on the classical boosting framework for hashing, while SNR maximization is a novel hashing framework with theoretical guarantee and great flexibility in designing …
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Face verification using high dimensional feature
… using high-dimensional feature. We first used Adaboost Cascade Classifier to detect face then using facial points detector get the points which we want to build the high-dimensional based on them. To the face verification problem, we used a �smart� algorithm Bayesian Face Revisited. Finally, we …
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Predicting Suicide Risk Among Youths Using Machine Learning Methods
… regression (LR), multilayer perceptron (MLP), AdaBoost (Ada), random forest (RF), and bagging using YRBSS dataset and investigate the effectiveness of several data handling techniques to improve the overall performance of suicide risk prediction.</p> <p>The dataset consists of 76 health …
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Phased Array Damage Detection and Damage Classification in Guided Wave Structural Health Monitoring
… suitability. A machine learning algorithm called Adaboost is chosen due to its effectiveness and high accuracy performance. The classification is preformed using spectrograms and Adaboost for crack and corrosion damages. Artificial cracks and corrosions are created in Abaqus® to obtain the …
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A systematic study of offline recognition of Thai printed and handwritten characters
… percentage points). A boosting algorithm called AdaBoost yields a slight improvement in recognition rate (1.2 percentage points) over the original classifiers (without applying the AdaBoost algorithm).
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Automatic real-time facial expression recognition for signed language translation
… FACS action units based on Haar features and the Adaboost boosting algorithm. This method achieves equally high recognition accuracy for certain AUs but operates two orders of magnitude more quickly than the Gabor+SVM approach. Finally, we developed a software prototype of a real-time, automatic …
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Optimising credit card fraud detection through machine learning and deep learning with spatial-temporal imbalance handling
… (LR), Extreme Gradient Boosting (XGBoost), AdaBoost, Gaussian Naive Bayes, and Extra Trees Classifier—and two deep learning models, Gated Recurrent Unit (GRU) and Neural Network (NN). Performance was evaluated using Recall, Precision, F1 Score, ROC-AUC Score, and Accuracy. The Bagging …
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Action recognition via sequence embedding
… salient weak classifiers are picked up by AdaBoost algorithm. Our approach enables robust action recognition in very challenging situations and the framework is validated based on four public standard datasets: the Weizmann dataset, the KTH dataset, IXMAS multi-view dataset and Rochester. …
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