Using Prior Knowledge to Select Training Data
Data visualisation and exploration with prior knowledge
... The data were sampled from a GTM with an 8 × 8 grid in the latent ...space using a 2 × 2 ...the data had a block diagonal covariance matrix and experiments were conducted with a range of levels of ...
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Using Prior Knowledge in the Design of Classifiers
... utilizing prior knowledge to design better perform- ing classifiers when sample sizes are ...results using the Zipf model show that the proposed paradigm yields improved classifiers that outperform ...
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Bayesian network prior: network analysis of biological data using external knowledge
... biological knowledge by incorporating only certain features, such as net- work topology or binding sites in promoter ...external knowledge are em- ...of prior knowledge, regardless of its ...
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The structure of prior knowledge
... qualitative data gathered for this ...these data were gathered and ...qualitative data generated in order to address my research ...to select a methodology by which my data can be ...to ...
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Evaluating Motivational Interviewing Measures of Knowledge and Skill Using Training Outcome Data
... of training across several populations including nurses (Bohman, Forsberg, Ghaderi & Rasmussen, 2013; El-Mallakh, Chlebowy, Wall, Myers & Cloud, 2012; Robbins, Pfeiffer, Maier, LaDrig & Berg-Smith, ...
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Integration of gene expression data with prior knowledge for network analysis and validation
... database knowledge with expression data, namely for instance Cytoscape [40] or TS-REX ...external data to its ...by using tissue-specific expression probabilities derived from EST libraries ...
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Block GTM: Incorporating prior knowledge of covariance structure in data visualisation
... understanding data sets. In- cluding prior knowledge from experts into probabilistic models for data exploration is important since it constrains models, which usually leads to more ...
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Using Language Modeling to Select Useful Annotation Data
... er, we conducted 50 supervised learning experi- ments. In each experiment one instance of this verb was selected at random and used for testing while the rest was used for training a maximum entropy model. The ...
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Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.
... study, this is not deterministic and produces different results when solving the same clustering problem multiple times. Indeed, when we counted the number of times the mNP and mNFa clusters were separated when repeat- ...
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Using Prior Domain Knowledge to Build Robust HMM-Based Semantic Tagger Trained on Completely Unannotated Data
... of prior domain knowledge to build an HMM-based semantic tagging model with four main virtues - namely, it is trained on completely unlabeled data, it offers high ambiguity res- olution ability, it ...
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A Bayesian framework for extracting human gait using strong prior knowledge
... of prior knowledge and learning from ...strong prior knowledge into a system for extracting human ...strong prior is built from a simple articulated model having both time-invariant ...
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Identification of Mechatronic Systems with Dynamic Neural Networks using Prior Knowledge
... Figure 8: Output signals of the SDNN modell ˆ˙ ϕ 2 and the real TMS ˙ ϕ 2 with resulting cost function. 8 displays the outputs of the TMS and the SDNN-model during the identification process for the first set of initial ...
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Extraction of chemical-induced diseases using prior knowledge and textual information
... The second subtask consists of extracting chemical- induced diseases (CIDs) and delivering the chemical-dis- ease pairs per document. Our team participated in both CDR subtasks. For the DNER subtask, we used our concept ...
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Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
... incorporating prior knowledge into NMT, how to combine multiple overlapping, arbitrary prior knowledge sources still remains a major ...to training objectives (Cohn et ...
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SELECT DATA BY REGION
... days prior to the date set for the General Meeting on first call, or, in specific cases, up to five days after that ...2009 using the voting list ...
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Using transactive memory systems to select and study a strategy for institutional knowledge retention
... The obstacles that were discovered regarding TMS, include lack of competence-based trust and the absence of knowledge sharing. Since these obstacles were results from open- ended questions and only 40.91% of the ...
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Encoding Prior Knowledge with Eigenword Embeddings
... not select just a subset of the results. 5.4 Evaluation Preliminary Experiments In our first set of ex- periments, we vary the dimension of the word em- bedding vectors. We try m ∈ { 50, 100, 200, 300 } . Our ...
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Encoding Prior Knowledge with Eigenword Embeddings
... vector training algo- rithms use co-occurrence within window-based con- texts to measure relatedness among ...encode prior knowledge directly to improve the quality of word ...antonyms) using ...
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SHrinkage Covariance Estimation Incorporating Prior Biological Knowledge with Applications to High-Dimensional Data
... 3.4 Application to GlobalANCOVA In the last few years, global testing methods have been proposed as a useful tool for the analysis of high-dimensional genomic data. Single variables are not always the primary ...
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Off to a Good Start: Using Clustering to Select the Initial Training Set in Active Learning
... the training data used. Building a training set requires a large number of historical labelled ...dressed using active learning (AL) (Cohn, Atlas, and Lad- ner 1994), a semi-supervised machine ...
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