• SOFI 2018 The State of Food Security and Nutrition in the World

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    High resolution esophageal pressure topography (EPT) is an evolutionary .. The closest equivalent to the IRP in conventional manometry is the 'LES relaxation pressure.' . contours are highlighted, 30 mmHg (black line) and 50 mmHg (blue line). . Intact contraction, 20 mmHg isobaric contour without large or small break.

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  • Machine Learning: What it is and why it matters . SAS

    Teal abstract honeycomb background with white line art overlay . This video is either unavailable or not supported in this browser . bigger, more complex data and deliver faster, more accurate results even on a very large scale. . Machine learning can be used to achieve higher levels of efficiency, particularly when.

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    allowing relational knowledge about interacting entities to be efficiently . Or in the case of node classification, one might want to include information .. following: if we can learn to decode high dimensional graph informationsuch as the global positions of nodes ... Large scale information network embeddings (LINE).

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  • Using categorical data in machine learning with python: from dummy .

    Sep 19, 2017 . High cardinality categorical variables may have a very large number of . categorical data doesn't contain the same context or information that we . We will evaluate every method on a sample of 2M rows from the Avatzo CTR . and Random Forest algorithms to evaluate it's performance. . les.append(le)

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    Aug 31, 2016 . Straight and thick cucumbers with a vivid color and lots of prickles are considered premium grade and command much higher prices on the.

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  • ImageNet Classification with Deep Convolutional Neural Networks

    high resolution images in the ImageNet LSVRC 2010 contest into the 1000 dif . Despite the attractive qualities of CNNs, and despite the relative efficiency of their local . Challenge, an annual competition called the ImageNet Large Scale Visual . scheme that we employ essentially puts half of the kernels (or neurons) on.

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  • Using categorical data in machine learning with python: from dummy .

    Sep 19, 2017 . High cardinality categorical variables may have a very large number of . categorical data doesn't contain the same context or information that we . We will evaluate every method on a sample of 2M rows from the Avatzo CTR . and Random Forest algorithms to evaluate it's performance. . les.append(le)

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  • GitHub josephmisiti/awesome machine learning: A curated list of .

    xLearn A high performance, easy to use, and scalable machine learning . useful for solving machine learning problems on large scale sparse data, which is very . as the Charniak Johnson parser). colibri core C++ library, command line tools, ... Training a deep autoencoder or a classifier on MNIST digits Training a.

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    Dec 17, 2017 . Linear classification algorithms assume that classes can be separated by . Typically, algorithms with large numbers parameters require the most trial and . As mentioned previously, linear regression fits a line (or plane, or hyperplane) to the data set. . This high performance doesn't come for free, though.

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  • List of datasets for machine learning research Wikipedia

    These datasets are used for machine learning research and have been cited in peer reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms . High quality labeled training datasets for supervised and semi supervised.

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  • Label Filters for Large Scale Multilabel Classification Alexandru .

    where computationally efficient label filters pre select a small set .. of training large scale multilabel classifiers is an im . response is required in production; or in high volume streaming . the filtering line allows retrieving candidate labels in.

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  • Classes, Workshops, Training . NVIDIA Deep Learning Institute

    DLI electives explore how to apply a specific technology or development technique in two hours. Like full length courses, electives can be taken anytime, anywhere, with access to GPUs . and other strategies to increase performance and capability; Deploy your neural .. Image Classification with Microsoft Cognitive Toolkit.

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  • ImageNet Classification with Deep Convolutional Neural Networks

    high resolution images in the ImageNet LSVRC 2010 contest into the 1000 dif . Despite the attractive qualities of CNNs, and despite the relative efficiency of their local . Challenge, an annual competition called the ImageNet Large Scale Visual . scheme that we employ essentially puts half of the kernels (or neurons) on.

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  • How to choose algorithms Azure Machine Learning Studio .

    Dec 17, 2017 . Linear classification algorithms assume that classes can be separated by . Typically, algorithms with large numbers parameters require the most trial and . As mentioned previously, linear regression fits a line (or plane, or hyperplane) to the data set. . This high performance doesn't come for free, though.

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    Perform binary classification via SVM using separating hyperplanes and kernel transformations. . The best hyperplane for an SVM means the one with the largest margin between ... The negative class is the first element (or row of a character array), e.g., . The software uses a heuristic procedure to select the kernel scale.

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  • List of datasets for machine learning research Wikipedia

    These datasets are used for machine learning research and have been cited in peer reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms . High quality labeled training datasets for supervised and semi supervised.

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  • Representation Learning on Graphs: Methods and . CS @ Stanford

    allowing relational knowledge about interacting entities to be efficiently . Or in the case of node classification, one might want to include information .. following: if we can learn to decode high dimensional graph informationsuch as the global positions of nodes ... Large scale information network embeddings (LINE).

    contact us
  • Machine Learning: What it is and why it matters . SAS

    Teal abstract honeycomb background with white line art overlay . This video is either unavailable or not supported in this browser . bigger, more complex data and deliver faster, more accurate results even on a very large scale. . Machine learning can be used to achieve higher levels of efficiency, particularly when.

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  • randomForest R Project

    Title Breiman and Cutler's Random Forests for Classification and. Regression. Version .. diagonal and in [0, 1] off the diagonal (the order of row/column must match that of x). nNbr .. (or less), this symbol is adjusted by moving it up (or down) the scale. . Value. The output of cmdscale on 1 rf$proximity is returned invisibly.

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  • Label Filters for Large Scale Multilabel Classification Alexandru .

    where computationally efficient label filters pre select a small set .. of training large scale multilabel classifiers is an im . response is required in production; or in high volume streaming . the filtering line allows retrieving candidate labels in.

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  • spaCy · Industrial strength Natural Language Processing in Python

    spaCy excels at large scale information extraction tasks. . spaCy is designed to help you do real work to build real products, or gather real insights. . now the co founder and CEO of " u"online higher education startup Udacity, in an interview . are used, giving much better efficiency than the standard BiLSTM solution.

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  • k nearest neighbors algorithm Wikipedia

    In pattern recognition, the k nearest neighbors algorithm (k NN) is a non parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k NN is used for classification or regression: .. Another popular approach is to scale features by the mutual information of.

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  • randomForest R Project

    Title Breiman and Cutler's Random Forests for Classification and. Regression. Version .. diagonal and in [0, 1] off the diagonal (the order of row/column must match that of x). nNbr .. (or less), this symbol is adjusted by moving it up (or down) the scale. . Value. The output of cmdscale on 1 rf$proximity is returned invisibly.

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  • Fit multiclass models for support vector machines or other classifiers .

    Train an error correcting output codes (ECOC) multiclass model using support vector machine . Columns of CodingMat correspond to learners, and rows correspond to classes. ... Predictor data, specified as a full or sparse matrix. .. (cross validation loss), and rank of observation from smallest (best) to highest (worst).

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    Dec 11, 2008 . Although it is possible to conform either to WCAG 1.0 or to WCAG 2.0 (or .. of conformance are defined: A (lowest), AA, and AAA (highest). .. and alternative forms of CAPTCHA using output modes for different .. Large Text: Large scale text and images of large scale text have a contrast ratio of at least 3:1;.

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  • GitHub josephmisiti/awesome machine learning: A curated list of .

    xLearn A high performance, easy to use, and scalable machine learning . useful for solving machine learning problems on large scale sparse data, which is very . as the Charniak Johnson parser). colibri core C++ library, command line tools, ... Training a deep autoencoder or a classifier on MNIST digits Training a.

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  • Tutorials IJCAI 16

    Automatic affect detection and classification from text is a complex task in Artificial . often depend on information provided by other agents, whether it is for learning or . large scale synthetic populations by integrating data from multiple sources. .. This tutorial gives an overview of the state of the art in efficient and scalable.

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  • k nearest neighbors algorithm Wikipedia

    In pattern recognition, the k nearest neighbors algorithm (k NN) is a non parametric method used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k NN is used for classification or regression: .. Another popular approach is to scale features by the mutual information of.

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