[PDF] Top 20 Semi supervised deep rule based approach for image classification

... self-evolving semi-supervised learning strategy by exploiting the idea of “pseudo label” naturally with its prototype-based ...the semi-supervised DRB (SSDRB) classifier is able to ... See full document

... plentiful. Supervised approaches, however, are unable to use the unlabeled ...hand, semi-supervised machine learning approaches [13–16] consider both the labeled and unlabeled images for classifica- ... See full document

... training image into the size that the feature descriptor ...scene classification, is employed by the DRB system as the feature descriptor due to its simple structure and high performance ...input ... See full document

... new deep rule-based approach using high-level ensemble feature descriptor is proposed for aerial scene ...trained deep convolutional neural networks as the feature descriptor, the ... See full document

... our semi-supervised framework with a traditional supervised learn- ing method SVM, that performed the classification purely based on the exacted features from the stacked multi-modal ... See full document

... stochastic approach has an advantages like it will display the range of possible outcomes and subsequent decisions made after initial decision and disadvantages like it does not require training, so prior ... See full document

... is based on set of rules that a user has to comply with while using the ...a semi supervised approach is presented to extract catchphrases from software license agreement and provide meanings ... See full document

... traditional classification method based on supervised learning classifies remote sensing (RS) images by using sufficient labeled ...novel semi-supervised classification method ... See full document

... new approach based on the ensemble of the recently introduced deep (fuzzy) rule-based (DRB) classifiers [11], [12] is proposed for remote sensing scene ... See full document

... CNN models require vast amounts of labeled data to be trained properly; however, providing reliable annotated data to train the CNN models tends to be expensive. There are es- sentially two principal solutions that are ... See full document

... Corpus-based methods use a labeled corpus to train a sentiment classifier (Wan, 2009). Pang et al. (2002) apply machine learning approach to corpus-based sentiment classification firstly. They ... See full document

... data classification and pattern recognition ...data classification methods majorly divided into three categories such as supervised, classification and also semi-supervised ... See full document

... adopts supervised learning methods, since they achieve much better ...them, semi- supervised learning has received more and more attention ...hand, semi-supervised learning can ... See full document

... in semi-supervised learning assumes that there are two classes, and in each class there is a Gaussian ...any classification mistake can reinforce ... See full document

... sifier for the cosine similarity equal or greater than 0.8 which was chosen on the validation data. It must be noted that the result of the multi-class min-cut algorithm when we used all the neigh- bouring pages was much ... See full document

... the classification is not affected by an unfavourable segmentation, but it also means that a large number of segments (from a couple of dozen to a few hundred per swimming path) are ...manual classification ... See full document

... The maximum likelihood decision rule is based on the probability that a pixel belongs to a particular class. The Mahalanobis distance algorithm assumes that the histograms of the bands have normal ... See full document

... embedded semi-supervised feature selection algorithms mentioned above indiscrimi- nately and directly use the labeled and unlabeled samples to design the evaluation criterion or objective functions for ... See full document

... This classification is a pre- requisite for the task of learning attributes together with their values from text ...a semi-supervised manner, relying on observed low class volatility at the token ... See full document

... We evaluated our approach under a WEKA-based [23] framework running under Java JDK 1.6 with the libraries of MEKA and Mulan [21][22]. Jblas library for performing matrix operations while computing weights ... See full document