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貝葉斯分類器，bayesian classifier,貝葉斯分類器，bayesian classifier
上傳時間： 2015-09-14
上傳用戶：cylnpy
Boosting is a meta-learning approach that aims at combining an ensemble of weak classifiers to form a strong classifier. Adaptive Boosting (Adaboost) implements this idea as a greedy search for a linear combination of classifiers by overweighting the examples that are misclassified by each classifier. icsiboost implements Adaboost over stumps (one-level decision trees) on discrete and continuous attributes (words and real values). See http://en.wikipedia.org/wiki/AdaBoost and the papers by Y. Freund and R. Schapire for more details [1]. This approach is one of most efficient and simple to combine continuous and nominal values. Our implementation is aimed at allowing training from millions of examples by hundreds of features in a reasonable time/memory.
標簽： meta-learning classifiers combining Boosting
上傳時間： 2016-01-30
上傳用戶：songnanhua
自己編的matlab程序。用于模式識別中特征的提取。是特征提取中的Sequential Forward Selection方法，簡稱sfs.它可以結合Maximum-Likelihood-classifier分類器進行使用。
標簽： Sequential Selection Forward matlab
上傳時間： 2016-04-02
上傳用戶：ma1301115706
* acousticfeatures.m: Matlab script to generate training and testing files from event timeseries. * afm_mlpatterngen.m: Matlab script to extract feature information from acoustic event timeseries. * extractevents.m: Matlab script to extract event timeseries using the complete run timeseries and the ground truth/label information. * extractfeatures.m: Matlab script to extract feature information from all acoustic and seismic event timeseries for a given run and set of nodes. * sfm_mlpatterngen.m: Matlab script to extract feature information from esmic event timeseries. * ml_train1.m: Matlab script implementation of the Maximum Likelihood Training Module. ?ml_test1.m: Matlab script implementation of the Maximum Likelihood Testing Module. ?knn.m: Matlab script implementation of the k-Nearest Neighbor classifier Module.
標簽： acousticfeatures timeseries generate training
上傳時間： 2013-12-26
上傳用戶：牛布牛
Semantic analysis of multimedia content is an on going research area that has gained a lot of attention over the last few years. Additionally, machine learning techniques are widely used for multimedia analysis with great success. This work presents a combined approach to semantic adaptation of neural network classifiers in multimedia framework. It is based on a fuzzy reasoning engine which is able to evaluate the outputs and the confidence levels of the neural network classifier, using a knowledge base. Improved image segmentation results are obtained, which are used for adaptation of the network classifier, further increasing its ability to provide accurate classification of the specific content.
標簽： multimedia Semantic analysis research
上傳時間： 2016-11-24
上傳用戶：蟲蟲蟲蟲蟲蟲
AdaBoost, Adaptive Boosting, is a well-known meta machine learning algorithm that was proposed by Yoav Freund and Robert Schapire. In this project there two main files 1. ADABOOST_tr.m 2. ADABOOST_te.m to traing and test a user-coded learning (classification) algorithm with AdaBoost. A demo file (demo.m) is provided that demonstrates how these two files can be used with a classifier (basic threshold classifier) for two class classification problem.
標簽： well-known algorithm AdaBoost Adaptive
上傳時間： 2014-01-15
上傳用戶：qiaoyue
This is a case for recognition of hand gestures using the 7 Hu moments and neural network classifiers
標簽： recognition classifier gestures moments
上傳時間： 2017-08-06
上傳用戶：zhaiye
In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data classification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local scaling heuristic strategy, then optimize a pair of nonparallel hyperplanes through two smaller sized support vector machine (SVM)-typed problems. In the prediction stage, BDTWSVMs assign to the class label depending on the kernel density degree-based distances from each test point to the two hyperplanes. BDTWSVMs not only inherit good properties from twin support vector machines (TWSVMs) but also give good description for data points. The experimental results on toy as well as publicly available datasets indicate that BDTWSVMs compare favorably with classical SVMs and TWSVMs in terms of generalization
標簽： recognition Bi-density machines support pattern vector twin for
上傳時間： 2019-06-09
上傳用戶：lyaiqing
General paradigm in solving a computer vision problem is to represent a raw image using a more informative vector called feature vector and train a classifier on top of feature vectors collected from training set. From classification perspective, there are several off-the-shelf methods such as gradient boosting, random forest and support vector machines that are able to accurately model nonlinear decision boundaries. Hence, solving a computer vision problem mainly depends on the feature extraction algorithm
標簽： Convolutional Networks Neural Guide to
上傳時間： 2020-06-10
上傳用戶：shancjb
