Supervised Learning
Large Margin Semi-supervised Learning
... semi-supervised learning occurs when a large amount of unlabeled data is avail- able with only a small number of labeled ...semi-supervised learning methodology, using grouping information ...
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Supervised Learning Classification Algorithms Comparison
... machine learning uses supervised learning. In supervised learning, the machine learning models are first trained over dataset with associated correct ...
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A Supervised Learning Approach to Acronym Identification
... the supervised learning ...the supervised learning component, rather than by hand-coded ...the supervised learning component can easily be retrained for a new ...
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Multi-Objective Supervised Learning
... By extending the Gini coefficient analysis a random allocation simplex can be used to compare different classifiers in Q(Q − 1) dimensional objective space. Classifiers whose off-diagonal confusion rates sum to greater ...
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Towards deep semi supervised learning
... Since unsupervised methods for pre-training layers of neural networks were an essential part of the first wave of deep learning methods (Hinton et al., 2006; Vincent et al., 2008; Bengio, 2009), a natural next ...
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Weakly Supervised Learning of Objects and Attributes.
... To keep the computational cost manageable, object proposal gains attentions to re- duce the number of evaluated windows. Previous works demonstrate that the proposal algorithm can indeed significantly speed up the object ...
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Paradigm classification in supervised learning of morphology
... Overall, supervised and semi-supervised learning of morphology by generalizing patterns from inflec- tion tables is an active research field. Recent work sharing our goals includes Toutanova and ...
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Lγ-PageRank for semi-supervised learning
... Semi-Supervised Learning (G-SSL) is a modern important tool for classi- ...Unsupervised Learning fully relies on the data structure and Supervised Learning demands extensive labeled ...
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Towards Automated Semi-Supervised Learning
... Machine Learning (AutoML) aims to build an appropriate machine learning model for any unseen dataset automatically, ...supervised learning. In many applications, however, semi- ...
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On the Global Optimality of Model-Agnostic Meta-Learning: Reinforcement Learning and Supervised Learning
... reinforcement learning subtasks maps from an MDP T = (S, A, P, r, γ, ζ) to a policy π, aiming at maximizing the expected total reward J (π) defined in ...a supervised learning subtask aims at ...
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Unbiased Generative Semi-Supervised Learning
... examines learning the parameters of both a single gaussian and a GMM when labels are ...semi-supervised learning, in particular from the point of view of the Hughes phenomenon (Hughes, ...are ...
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Predicting Good Probabilities With Supervised Learning
... ten supervised learning algorithms: SVMs, neural nets, decision trees, memory-based learn- ing, bagged trees, random forests, boosted trees, boosted stumps, naive bayes and logistic ...some learning ...
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A Generative Model for Semi-Supervised Learning
... purely supervised models as well as semi- supervised models, demonstrating the effectiveness of latent representation learnt by VAE on the downstream classification ...
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Semi-Supervised Learning with Measure Propagation
... semi-supervised learning based on minimizing the Kullback-Leibler divergence between discrete probability measures that encode class membership ...semi-supervised learning problem on very ...
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Multi-Objective Supervised Learning
... A number of problems arise in supervised learning. On the data side there is the issue of how well the training data actually represents the generating process (e.g., if important relationships are not ...
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Semi-supervised learning
... semi-supervised learning algorithms in favorable situations has trig- gered several recent attempts (Balcan & Blum 2005, Ben-David, Lu & Pal 2008, Kaariainen 2005, Laf- ferty & Wasserman 2007, ...
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Extractive Summarization Using Supervised and Semi Supervised Learning
... a learning-based ap- proach to combine various sentence fea- ...encouraging, supervised learning approach requires much labeled ...semi- supervised learning approach achieves comparable ...
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Semi Supervised Learning for Relation Extraction
... This paper proposes a new effective and efficient semi-supervised learning method in relation ex- traction. First, a moderate number of weighted support vectors are bootstrapped from all the avail- able ...
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Supervised Learning via Euler's Elastica Models
... for supervised learning ...high-dimensional supervised learning problems? To the best of our knowledge, Varshney and Will- sky (2010) were the first attempt to propose level set based PDEs for ...
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Semi Supervised Learning of Concatenative Morphology
... Morphological analysis is required in many natu- ral language processing problems. Especially, in agglutinative and compounding languages, where each word form consists of a combination of stems and affixes, the number ...
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