Feature Learning
Discriminative Feature Learning for Unsupervised Video Summarization
... effective feature learning due to flat distribution of output importance scores and second, there is the training difficulty with long-length video ...
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Deep Unsupervised Feature Learning for Natural Language Processing
... deep learning methods for unsupervised feature learning for NLP ...deep learning methods are not a sil- ver bullet and do not always lead to improved re- ...
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Multi-Stage Multi-Task Feature Learning
... Multi-task feature learning, which aims to learn a common set of shared features, has received a lot of interests in machine learning recently, due to the popularity of various sparse learning ...
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Multiplicative Multitask Feature Learning
... Research efforts have been devoted to various MultiTask Feature Learning (MTFL) algo- rithms. One direction of these works directly learns the dependencies among tasks, either by modeling the correlated ...
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NILC at CWI 2018: Exploring Feature Engineering and Feature Learning
... a feature engineering method using lexical, n-gram and psycholin- guistic features, (ii) a shallow neural network method using only word embeddings, and (iii) a Long Short-Term Memory (LSTM) language model, which ...
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Adaptive Feature Ranking for Unsupervised Transfer Learning
... the learning in the target RBM depends on an influence factor θ; in this paper θ ∈ [0 ...influence learning in the target ...influence learning in the target domain. We refer to this case as adaptive ...
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Detection of Text based Cyberbullying using Semantic Enhanced Marginalized Denoising Autoencoder Learning Model
... by feature co-occurrence ...strong feature illustration below associate degree unsupervised learning framework, and this also motivates different progressive text feature learning ways ...
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Efficient and Scalable Multi-Task Regression on Massive Number of Tasks
... Single-task learning (STL), which learns a single model by pooling together the data from all the tasks and Independent task learning (ITL), which learns each task ...achieve learning general- ...
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Building Trainable Taggers in a Web based, UIMA Supported NLP Workbench
... sented as the Feature Generator, CRF++ Trainer and CRF++ Tagger blocks. Figure 2a shows a pro- cess of building a statistical model supported by a document reader, common, well-established pre- processing ...
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Study on Machine Learning and Deep Learning Methods for Human Action Recognition
... powerful feature which can be easily generalized, features learning using deep learning have gained great attention in recent ...deep learning in the identification of action has contributed ...
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Research on Chinese Micro blog Sentiment Classification Based on Recurrent Neural Network
... different feature extraction with Naive Bayesian (NB), support vector (SVM) and maximum entropy model (ME) to realize the sentiment classification of Twitter ...four learning models of polynomial naive ...
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Comparative of Data Mining Classification Algorithm (CDMCA) in Diabetes Disease Prediction
... The PLS Regression is initially defined for the prediction of continuous target variable. But it seems it can be useful in the supervised learning problem where we want to predict the values of discrete ...
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Living theory research as a way of life
... the learning of my pupils could not be subsumed within any conceptual framework in the psychology of education or any existing discipline of ...own learning and in the learning of ...
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Text feature extraction based on deep learning: a review
... deep learning is that these layers of features are not designed by human engineers, they are learned from data using a general purpose learning pro- cedure ...Deep learning requires very little ...
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The Role of Frontline Leadership in Organizational Learning: Evidence from Incremental Business Process Improvement
... Although these data collected through multiple sources need to be used for classification in order to achieve higher classification accuracy and robustness, unfortunately, they are heterogeneous and have different ...
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Predicting Diabetes By Cosequencing The Various Data Mining Classification Techniques
... a learning system needs to be evaluated before it can become ...machine learning systems ...the learning system, and the remaining data that have not been sampled are used to test the ...
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Application of Different Feature Weights Based on Learning Feature Dictionary for Image Super Resolution
... dictionary learning method, has a comparatively improved shape similar to the original image, but has a problem in that the results are different according to the time required for the learning process and ...
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Vision and Feature Norms: Improving automatic feature norm learning through cross modal maps
... Property norms have the potential to aid a wide range of semantic tasks, provided that they can be obtained for large numbers of concepts. Recent work has focused on text as the main source of information for auto- matic ...
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Behavior Analysis from Videos using Motion based Feature Extraction
... In image processing and computer vision, a feature is a piece of information which is applicable for solving the computational work affiliated to a certain application. Features may be specific cognition in the ...
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An Optimal Machine Learning Approach for Large-scale Applications
... by learning with partial inputs: where only a limited number of features is allowed to be accessed for each instance by the ...online feature selection approach with partial input information by employing a ...
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