supervised training
Lightly Supervised Training for Hierarchical Phrase Based Machine Translation
... lightly-supervised training would be to concatenate the human-generated parallel data and the unsupervised data and to jointly extract phrases from the unified parallel data (after having trained word ...
6
Improving Twitter Sentiment Analysis with Topic Based Mixture Modeling and Semi Supervised Training
... semi-supervised training (about 100K). It serves as another baseline with more training data, for a fair comparison with the topic-based mixture modeling that uses the same amount of training ...
6
Improved Discriminative ITG Alignment using Hierarchical Phrase Pairs and Semi supervised Training
... Word pairs are collected over Viterbi align- ment and their conditional probabilities are esti- mated by MLE. As to phrase/h-phrase, if they are handled in a similar way, then there will be data sparseness (as there are ...
9
Semi Supervised Training for the Averaged Perceptron POS Tagger
... The supervised training described in (Collins, 2002) uses manually annotated data for the esti- mation of the weight coefficients ...other training parameters, such as the number of iterations, ...
9
Semi Supervised Training for Statistical Word Alignment
... a supervised fashion using the labeled data, and this supervised model is used to attach labels (or a probability dis- tribution over labels) to the unlabeled data, then a new supervised model is ...
8
Minimizing Manual Annotation Cost in Supervised Training from Corpora
... We investigate the committee-based method, where the learning algorithm evaluates an example by giving it to a committee containing several variant models, all 'consistent' with the trai[r] ...
8
Relabeling Distantly Supervised Training Data for Temporal Knowledge Base Population
... To more closely examine the effects of relabeling on classification, we compared the accuracy of the labels before and after relabeling for the spouse slot type using development data. Since the set of all in- stances ...
6
Weakly Supervised Training of Semantic Parsers
... We present a method for training a semantic parser using only a knowledge base and an un- labeled text corpus, without any individually annotated sentences. Our key observation is that multiple forms of weak ...
12
Semi supervised Training Data Generation for Multilingual Question Answering
... Finally, for RQ3, we train a QA system for Korean QA, combining both small-scale annotated data and large-scale translated resources we discussed above. As motivated from RQ1, there still remains the problem that the ...
5
A graphical heuristic for reduction and partitioning of large datasets for scalable supervised training
... One research area where training set selection has been given attention to is sup- port vector machines (SVM). Generally, these selection methods can be divided into two types, primarily based on whether n is ...
35
Semi supervised training of a Kernel PCA Based Model for Word Sense Disambiguation
... The KPCA model can fail when it encounters a target word whose context contains a combination of features that may in fact be interdependent, but are not similar to any combinations that occurred in the limited amounts ...
7
Semi Supervised Convex Training for Dependency Parsing
... efficient training procedure that can find a global minimum, and, perhaps surprisingly, can systemat- ically improve the accuracy of supervised training approaches for learning dependency ...
9
Neural Adversarial Training for Semi supervised Japanese Predicate argument Structure Analysis
... pseudo training data from a raw corpus and use them for their zero pronoun resolu- tion ...pseudo training data by dropping certain words or pronouns in a raw corpus and assuming them as correct ...pseudo ...
11
Multi-fold MIL Training for Weakly Supervised Object Localization
... Standard supervised training requires bounding box annotations of object ...the supervised informa- tion is restricted to binary labels that indicate the ab- sence/presence of object instances in the ...
9
Co-Training of Audio and Video Representations from Self-Supervised Temporal Synchronization
... We have shown that optimizing the correspondence of audio-visual representations can be used to learn powerful representation in both audio and video domains. Our audio representations were powerful even when trained on ...
14
A Supervised Machine Learning Approach with Re-training for Unstructured Document Classification in UBE
... We designed the system to predict the classification category of the given UBE under question. We considered only first three classification results because as the category classification position increases, its ...
12
Supervised Versus Home Exercise Training Programs on Functional Balance in Older Subjects
... an additional target to confirm improvements in functional activities for older participants. The non-significant increases in muscle strength after a home exercise program may be due to certain limiting factors and ...
11
Reserved Self training: A Semi supervised Sentiment Classification Method for Chinese Microblogs
... starts training on labeled da- ta only, after each iteration, the most confident- ly predicted unlabeled samples would be incorpo- rated as additional labeled data, decided by con- fidence scores calculation ...ly ...
8
Supervised Classification Using Balanced Training
... balanced training data in building our classifiers, we com- pare two sets of classifiers, built using two distinct training pools: one balanced, under-sampled training set and one unbalanced ...
12
Weakly supervised pedestrian detector training by unsupervised prior learning and cue fusion in videos
... During training (including unsupervised prior learning), we only use the first ...weakly supervised algorithm (2) the detector obtained by strong supervision (manual bounding box anno- tation) and (3) the ...
6