supervised method
Semi-supervised method for biomedical event extraction
... Methods: In this study, a semi-supervised method for combining labeled data with large scale of unlabeled data is presented to improve the performance of biomedical event extraction. We propose a set of ...
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Supervised Method for Face Photo Sketch Synthesis and Recognition
... Face sketch recognition is a challenging as well as important recognition technique. There is a need for continual research on forensic sketch matching. This can help assist the law enforcement agencies to apprehend ...
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A Semi Supervised Method to Learn and Construct Taxonomies Using the Web
... Although many algorithms have been devel- oped to harvest lexical resources, few organize the mined terms into taxonomies. We pro- pose (1) a semi-supervised algorithm that uses a root concept, a basic level ...
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Title: Improved Optimized Sentiment Classification On Dynamic Tweets
... learning method based on random subspace generation which dynamically generates various subspaces in the iteration process to guarantee enough variation among the involved ...semi-supervised method ...
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Partitioning The Documents Based On Semi-supervised Clustering Method.
... ABSTRACT: One crucial role of document clustering is to examine the number of clusters in an appropriate way from the given dataset to which documents should be partitioned. In this paper, we propose a novel approach, ...
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Evaluating the Extraction Approaches of Flood Extended Area by Using ALOS 2/PALSAR 2 Images as a Rapid Response to Flood Disaster
... backscattering method displayed the flooding as one unit ...and supervised method, especially in the top of the study ...and supervised classification, the area that was wet due to the rain ...
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Cross lingual Projected Expectation Regularization for Weakly Supervised Learning
... semi-supervised method for training discriminative models by projecting expectations across ...our method, learned over bitext alone, can rival per- formance of supervised models trained with ...
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A High Performance Semi Supervised Learning Method for Text Chunking
... The contributions of this paper are two-fold. First, we present a novel robust semi-supervised method based on a new learning model and its application to chunking tasks. Second, we report higher per- ...
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A Network Intrusion Detection System Using Clustering and Outlier Detection
... a supervised method, and an outlier-based ...of supervised or outlier-based classifier at a particular level for a given dataset is based on the classification accuracy of the individual classifier ...
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SESS: A Self-Supervised and Syntax-Based Method for Sentiment Classification
... The supervised method in phase 2 requires the training data to be both adequate and precisely ...the supervised method surely suffers bad performance no matter how powerful the method ...
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A Cross Topic Method for Supervised Relevance Classification
... a supervised approach. As to the supervised method, traditionally, a specific topic-oriented classifier could be trained for prediction on a single topic (Hasan and Ng, 2013; Y Wang et ...this ...
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Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach
... semi-supervised method is found in Liu and Chandrasekar (2000), in which employing neu- ral networks in updating the membership functions reduces classification algorithm to a neuro-fuzzy ...based ...
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Word Representations: A Simple and General Method for Semi Supervised Learning
... existing supervised NLP sys- tem, a simple and general way to improve accuracy is to use unsupervised word representations as extra word ...state-of-the-art supervised baselines, and find that each of the ...
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Twitter Sentiment Classification Using Supervised Lazy Learning Method
... I feel great pleasure in submitting this paper on “Twitter Sentiment Classification using supervised lazy learning method”. I wish to express true sense of gratitude towards my Principal Dr. R. P. Singh and ...
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A SUPERVISED LEARNING METHOD TO CLUSTER XML DOCUMENTS WITH REDUCED COMPLEXITY
... [Yoon, Jong P., Vijay Raghavan, and Venu Chakilam, 2001] have proposed the BitCube – a three dimensional bitmap indexing form XML documents in which matrix construction time is noticeable. According to this ...
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Classification of uterine EMG signals using supervised classification method
... using supervised classification ...their supervised classification into independent grou- ps: preterm deliveries (PD) and deliveries at term (TD) according to their frequency ...
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Discriminant non-stationary signal features’ clustering using hard and fuzzy cluster labeling
... and supervised learning approaches. This method applied an unsupervised clustering to the fea- ture vectors from all the different classes, and then used a supervised labeling method to select ...
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Power Transformer Fault Diagnosis based on Deep Learning
... for the weight of sparse penalty item. Throughout the encoding process, ultimately, we need get the optimized connection weight W and bias b. And the sparse cost function C sparse is a function with W and b as its ...
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A Hybrid Statistical Approach for Named Entity Recognition for Malayalam Language
... learning method is another type of machine learning model, where an unsu- pervised model learns without any ...un- supervised learning are usually not completely ...
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FCM BPSO: ENERGY EFFICIENT TASK BASED LOAD BALANCING IN CLOUD COMPUTING
... In [5], the MERGE system (Maximum Entropy Rule Guided Extractor) is presented, which uses the Maximum Entropy Markov Model (MEMM). For each token, a feature vector is built in a special way according to a certain set of ...
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