class-labeled training data
Closed Sets for Labeled Data
... on labeled data (learning classification rules, subgroup dis- covery, or contrast set mining) as a rule induction problem, that is, a process of searching a space of concept descriptions (hypotheses in the ...
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A Survey: Analysis of Current Approaches in Anomaly Detection
... a training dataset (i.e., data instances labeled as normal or attack ...unseen data instance is comparedagainst the model to determine which class it belongs ...the training ...
5
Neural Self Training through Spaced Repetition
... the class, and each view should be sufficient, i.e., the class of an instance can be accurately predicted from each view ...unlabeled data are then used to iteratively construct addi- tional ...
11
Automatic prediction of aspectual class of verbs in context
... aspectual class, includ- ing a new set of distributional ...new data sets of clauses la- beled for aspectual ...where labeled training data is available, improvement over the most ...
7
Exploiting Class Learnability in Noisy Data
... sufficient labeled training data for supervised machine learning requires easily accessible but noisy sources, such as crowdsourcing services or tagged Web ...in data sets harvested via these ...
8
On Semi Supervised Learning of Gaussian Mixture Models for Phonetic Classification
... unknown class labels of the unlabeled data as hidden variables and used the expectation-maximization (EM) algorithm to optimize the joint likelihood of labeled and unlabeled ...unlabeled data ...
9
Article Description
... (or training documents) from each class are required for training the ...the training documents come from a person who has labeled them where labeling refers to the process of ...
7
Clustering based Two-Stage Text Classification Requiring Minimal Training Data
... unlabeled data, one assumption which is made, explicitly or implicitly, by most of the semi-supervised learning algorithms is the so-called cluster assumption that two points are likely to have the same ...
18
A Review on Machine Learning Algorithms
... a class of machine learning tasks and techniques that also make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled ...
5
Clustering High Dimensional Data Using Fast Algorithm
... both labeled and unlabeled data for training typically a small amount of labeled data and a large amount of unlabeled ...the data from training data or ...
7
Word Sense Disambiguation Using Label Propagation Based Semi Supervised Learning
... Finally we ran LP on a connected graph-minimum spanning tree generated for this dataset, shown in Figure 2(a). A, B, C represent three points, and the edge A − B connects the two moons. Figure 2(b)- 2(f) shows the ...
8
DCFEE: A Document level Chinese Financial Event Extraction System based on Automatically Labeled Training Data
... structured data can be mapped to the event arguments within the ...document-level data, as shown on the right of Figure 3. The document-level data and the sentence-level data together form the ...
6
Compensating for Annotation Errors in Training a Relation Extractor
... State-of-the-art supervised methods for relation extraction also differ from each other on data representation. Given a relation mention, feature-based methods (Miller et al., 2000; Kambhatla, 2004; Boschee et ...
10
Mixture of Expert/Imitator Networks: Scalable Semi-Supervised Learning Framework
... on training baseline DNNs (LSTM, LM-LSTM, and ADV-LM-LSTM) with incorporating random vectors as the replacement of IMNs, which is denoted as “+IMN (Ran- ...
9
An Approach to Summarizing Short Stories
... Yet, such expressions are not easy to capture automatically. In order to use the information expressed in temporal adverbials, the author ana- lyzed the training data for presence of such ex- pressions and ...
8
A participatory sensing framework to classify road surface quality
... As future work, we first intend to improve the graphical interface to encourage more users gathering and label- ing road surface quality. Next, we plan to provide an API to make the classification available for other ...
16
Integrating Knowledge Resources and Shallow Language Processing for Question Classification
... ingful and easier to find the answers. Similarly, the question “what is Bill Gates of Microsoft e- mail address” ? is labeled as LOC:Other by the authors. If this question is searched using a search engine, a lot ...
7
Survey on Naive Bayes Algorithm
... the data, but the Gaussian (or Normal distribution) is the easiest to work with because you only need to estimate the mean and the standard deviation from your training ...
7
Subjectivity Recognition on Word Senses via Semi supervised Mincuts
... Both Wiebe and Mihalcea (2006) and our prior work (Su and Markert, 2008) present an annota- tion scheme for word sense subjectivity and algo- rithms for automatic classification. Wiebe and Mi- halcea (2006) use an ...
9
Dependency Parser Adaptation with Subtrees from Auto Parsed Target Domain Data
... There are roughly two varieties of domain adap- tation problem—fully supervised case in which there are a small amount of labeled data in the target domain, and semi-supervised case in which there are no ...
6