List of datasets in computer vision and image processing
Outline of machine learning
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Labeled data is a group of samples that have been tagged with one or more labels. Labeling typically takes a set of unlabeled data and augments each piece of it with informative tags. For example, a data label might indicate whether a photo contains a horse or a cow, which words were uttered in an audio recording, what type of action is being performed in a video, what the topic of a news article is, what the overall sentiment of a tweet is, or whether a dot in an X-ray is a tumor.
Labels can be obtained by asking humans to make judgments about a given piece of unlabeled data. Labeled data is significantly more expensive to obtain than the raw unlabeled data.
Labeleddata is a group of samples that have been tagged with one or more labels. Labeling typically takes a set of unlabeled data and augments each piece...
of the training data. The remaining data is unlabeled or imprecisely labeled. Intuitively, it can be seen as an exam and labeleddata as sample problems...
in San Francisco in the US State of California. The company provides labeleddata used to train AI applications. Scale was founded in 2016 by Alexandr...
issue to data stream mining. Other challenges that arise when applying machine learning to streaming data include: partially and delayed labeleddata, recovery...
BSTs were devised in the 1960s for the problem of efficient storage of labeleddata and are attributed to Conway Berners-Lee and David Wheeler. The performance...
big data and a new abundance of processing power. Pattern recognition systems are commonly trained from labeled "training" data. When no labeleddata are...
using labeled input data. Labeleddata includes input-label pairs where the input is given to the model and it must produce the ground truth label as the...
a "small" set of labeled target examples. The supervised domain adaptation: all the examples considered are supposed to be labeled. The objective is...
battle; the dragon is green, and labeled "Complexity of Compiler Design", while the knight wields a lance and a shield labeled "LALR parser generator" and...
appropriate label to be affixed, labels the packet accordingly, and then forwards the labeled packet into the MPLS domain. Likewise, upon receiving a labeled packet...
data. CP works by computing nonconformity scores on previously labeleddata, and using these to create prediction sets on a new (unlabeled) test data...
material content of a label should comply with applicable regulations. Life cycle assessments of the item being labeled and of the label itself are useful...
obtained. Data may be numerical or categorical (i.e., a text label for numbers). Data is collected from a variety of sources. A list of data sources are...
favorable a review is for the product. Such an analysis may need a labeleddata set or labeling of the affectivity of words. Resources for affectivity of words...
vector machines extend SVMs in that they could also treat partially labeleddata in semi-supervised learning by following the principles of transduction...
of labeled isotopes (i.e. 30% uniformly labeled 13C glucose contains a mixture that is 30% labeled with 13 carbon isotope and 70% naturally labeled carbon)...
Wayback Machine Kevin K. Duh (2009), Learning to Rank with Partially-LabeledData (PDF), archived (PDF) from the original on 2011-07-20, retrieved 2009-12-27...
graph hosted by the Wikimedia Foundation. It is a common source of open data that Wikimedia projects such as Wikipedia, and anyone else, is able to use...
polygons. Carriageways and cadastres are examples of feature data. Features can be labeled when displayed on a map. The definition of features that share...
in bioinformatics is labeling new genomic data (such as genomes of unculturable bacteria) based on a model of already labeleddata. Hidden Markov models...
heat maps to the right, labeled "Data Analysis Heat Map Example," show different ways in which one may present genomic data over a specific region (Hist1...
from labeleddata, it's possible to construct a semi-supervised training algorithm that can learn from a combination of labeled and unlabeled data by running...