learning from training samples
Big Generalizations with Small Data: Exploring the Role of Training Samples in Learning Adjectives of Size
... ‘small’) from visually-grounded con- texts (MALeViC; Pezzelle and Fern´andez, ...Differently from standard approaches in language and vision treating size as a fixed attribute of ob- jects (Johnson et ...
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Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References
... In recent years, open-domain dialogue genera- tion has become a research hotspot in Natural Language Processing due to its broad applica- tion prospect, including chatbots, virtual personal assistants, etc. Though plenty ...
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Hinge-Minimax Learner for the Ensemble of Hyperplanes
... transfer learning settings has significant benefits compared to NN (standard settings), in both classification accuracy and training ...stems from the ability of LHM model to learn from ...
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Batch IS NOT Heavy: Learning Word Representations From All Samples
... batch learning. To address the efficiency challenge in learning from all training samples, we devise a regression-based loss function for word embed- ding, which allows fast ...
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Text Emotion Distribution Learning from Small Sample: A Meta Learning Approach
... distribution learning (EDL) aims to develop models that can predict the inten- sity values of a sentence across a set of emo- tion ...supervised learning require a large amount of well-labelled ...
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Bias Reduction via End-to-End Shift Learning: Application to Citizen Science
... end-to-end learning scheme, which we call the Shift Compensation Network (SCN), that estimates the shift factor while re-weighting the training data to correct the ...by learning a discriminator that ...
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Research on Freeway Passenger Flow Prediction Based on Neural Network
... of learning algorithm, whose probably thought is: input learning samples, using gradient descent method on the network weights and biases continuously ...
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Active learning methods for classification and regression problems
... the samples distant from the current support vectors are ...active learning approach is used for regression problems in the chemometrics ...concentrations from spectroscopic ...adding ...
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A Fast Boosting based Learner for Feature Rich Tagging and Chunking
... for learning rules represented by combina- tion of features. Our learning algorithm uses the following methods to learn rules from large-scale training samples in a short time while ...
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Title: An Endowed Takagi-Sugeno-type Fuzzy Model for Classification Problems
... incremental learning approach. Margin selective gradient descent learning and incremental support vector machine helps the proposed fuzzy model to learn with high generalization ...ability. Training ...
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Parsimonious Random Vector Functional Link Network for Data Streams
... batch learning scenario and lack a self-organizing ...configured from scratch and can be automatically generated, pruned and recalled from data ...important training samples for model ...
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Generative Models from the perspective of Continual Learning
... Continual Learning is Variational Continual Learning (VCL) [3], which adapts variational inference to a continual ...update from one task to another inspired from Bayes’ ...representations ...
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Online Learning with Samples Drawn from Non-identical Distributions
... of learning theory, samples for algorithms are often assumed to be drawn indepen- dently from an identical ...with samples drawn from non- identical ...online learning for least ...
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On the Hardness of Learning with Rounding over Small Modulus
... of q. If the entries of the secret s are bits, s is then fully recovered given LWR samples. By Theorem 1 the learner’s advantage does not deteriorate significantly when the LWR samples are replaced by LWE ...
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A Sparse Autoencoder and Softmax Regression based
... unlabeled pre-training samples, 420 images as labeled training samples, and the remaining 211. 280 images as testing samples.[r] ...
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Random subspacing for regression ensembles
... the training data to a test instance, a base model is chosen which has the lowest training ...other learning mod- els in a localised region of the instance space even if, on average over the whole ...
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Management development from the perspective of small firm owner-managers
... advice from accountant of bank manager 64% Getting advice from a business mentor or coach 44% Joining a group of business owners discussing current issues 36% Getting advice from CoCs, EDAs or ...
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High-performance on-road vehicle detection with non-biased cascade classifier by weight-balanced training
... these samples are updated by applying the selected weak classifiers using the same process in ...negative samples in the reservoir set, is regarded as the degree of weight unbalance in the positive/negative ...
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Partially Distribution Free Learning of Regular Languages from Positive Samples
... The major weakness of this approach in our opinion is that the parameter n in the sample complexity polynomial is the number of states in the PDFA generating the distribution, and not the number of states in the minimal ...
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Dessler_HRM12e_PPT_08.ppt
... • Types of Programmed Learning Types of Programmed Learning Interactive multimedia training Interactive multimedia training. Virtual reality training Virtual reality training Virtu[r] ...
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