|Yakmo||Yakmo implements robust, efficient k-means clustering with triangular inequality and smart initialization , while supporting alternative clustering outputs. The use of the triangular inequality allows k-means to skip unnecessary distance calculations, while the smart initialization by randomized seeding (k-means++) not only improves solution accuracy but also accelerates the convergence of the algorithm. In addition, you can obtain alternative clusterings via orthogonalization .
|YARN||MapReduce has undergone a complete overhaul in hadoop-0.23 and we now have, what we call, MapReduce 2.0 (MRv2) or YARN.
The fundamental idea of MRv2 is to split up the two major functionalities of the JobTracker, resource management and job scheduling/monitoring, into separate daemons. The idea is to have a global ResourceManager (RM) and per-application ApplicationMaster (AM). An application is either a single job in the classical sense of Map-Reduce jobs or a DAG of jobs.
The ResourceManager and per-node slave, the NodeManager (NM), form the data-computation framework. The ResourceManager is the ultimate authority that arbitrates resources among all the applications in the system.
The per-application ApplicationMaster is, in effect, a framework specific library and is tasked with negotiating resources from the ResourceManager and working with the NodeManager(s) to execute and monitor the tasks.
|YASENN||We introduce a novel approach to feed-forward neural network interpretation based on partitioning the space of sequences of neuron activations. In line with this approach, we propose a model-specific interpretation method, called YASENN. Our method inherits many advantages of model-agnostic distillation, such as an ability to focus on the particular input region and to express an explanation in terms of features different from those observed by a neural network. Moreover, examination of distillation error makes the method applicable to the problems with low tolerance to interpretation mistakes. Technically, YASENN distills the network with an ensemble of layer-wise gradient boosting decision trees and encodes the sequences of neuron activations with leaf indices. The finite number of unique codes induces a partitioning of the input space. Each partition may be described in a variety of ways, including examination of an interpretable model (e.g. a logistic regression or a decision tree) trained to discriminate between objects of those partitions. Our experiments provide an intuition behind the method and demonstrate revealed artifacts in neural network decision making.|
|YCML||A Machine Learning framework for Objective-C and Swift (OS X / iOS)|
|YEDDA||In this paper, we introduce YEDDA, a lightweight but efficient open-source tool for text span annotation. YEDDA provides a systematic solution for text span annotation, ranging from collaborative user annotation to administrator evaluation and analysis. It overcomes the low efficiency of traditional text annotation tools by annotating entities through both command line and shortcut keys, which are configurable with custom labels. YEDDA also gives intelligent recommendations by training a predictive model using the up-to-date annotated text. An administrator client is developed to evaluate annotation quality of multiple annotators and generate detailed comparison report for each annotator pair. YEDDA is developed based on Tkinter and is compatible with all major operating systems.|
|Yellowbrick||Yellowbrick is a suite of visual diagnostic tools called ‘Visualizers’ that extend the scikit-learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines scikit-learn with matplotlib in the best tradition of the scikit-learn documentation, but to produce visualizations for your machine learning workflow! For complete documentation on the Yellowbrick API, a gallery of available visualizers, the contributor’s guide, tutorials and teaching resources, frequently asked questions, and more, please visit our documentation at www.scikit-yb.org.|
|Yinyang K-means||This paper presents Yinyang K-means, a new algorithm for K-means clustering. By clustering the centers in the initial stage, and leveraging efficiently maintained lower and upper bounds between a point and centers, it more effectively avoids unnecessary distance calculations than prior algorithms. It significantly outperforms classic K-means and prior alternative K-means algorithms consistently across all experimented data sets, cluster numbers, and machine configurations. The consistent, superior performance-plus its simplicity, user-control of overheads, and guarantee in producing the same clustering results as the standard K-means does-makes Yinyang K-means a drop-in replacement of the classic K-means with an order of magnitude higher performance.|
|You Only Look Once
|We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is far less likely to predict false detections where nothing exists. Finally, YOLO learns very general representations of objects. It outperforms all other detection methods, including DPM and R-CNN, by a wide margin when generalizing from natural images to artwork on both the Picasso Dataset and the People-Art Dataset.|
|Youden Plot||The data for a Youden plot is generated by providing a number of laboratories aliquots from two separate unknown samples, which we will call A and B. Every lab analyzes both samples and a scatter plot of the A and B results are generated-the A results on the x -axis and the B results on the y -axis. Once this is completed, limits of acceptability are plotted and outliers can be identified.|
|Youden’s J Statistic||Youden’s J statistic (also called Youden’s index) is a single statistic that captures the performance of a diagnostic test.|