LUM Series Superfine Vertical Roller Grinding Mill
LUM Series Superfine Vertical Roller Grinding Mill

high performance cost ratio spiral classifier

  • ct scan

    spinning tube, commonly called spiral ct, or helical ct is an imaging technique in which an entire x-ray tube is spun around the central axis of the area being scanned. these are the dominant type of scanners on the market because they have been manufactured longer and offer a lower cost of production and purchase. the main limitation of this .

  • spiral ground heat exchanger

    spiral ghes are favorable due to their high efficiencies and low initial costs. when spiral ghes are connected to the heat pump system and total system gshp is integrated to the buildings, heating/cooling demands of that specific residential building could be supplied easily by spiral ghes. many researches show that when nine numbers of .

  • coda mixed-use development

    coda is a mixed-use development at tech square in midtown atlanta.the 770,000-square-foot 72,000 m 2 building contains 645,000 square feet 59,900 m 2 of office space, 80,000 square feet 7,400 m 2 of 'high performance computing space/data center', 30,000 square feet 2,800 m 2 of street level retail space, and a 20,000-square-foot 1,900 m 2 'outdoor living room'.

  • clarifier

    the fine particles then build up into a larger mass which then slides down the tube channels. the reduction in solids present in the outflow allows a reduction in the clarifier footprint when designing. tubes made of pvc plastic are a minor cost in clarifier design improvements and may lead to an increase of operating rate of 2 to 4 times.

  • hpcc

    hpcc high-performance computing cluster , also known as das data analytics supercomputer , is an open source, data-intensive computing system platform developed by lexisnexis risk solutions.the hpcc platform incorporates a software architecture implemented on commodity computing clusters to provide high-performance, data-parallel processing for applications utilizing big data.

  • sampling statistics

    the minimax sampling has its origin in anderson minimax ratio whose value is proved to be 0.5: in a binary classification, the class-sample sizes should be chosen equally. this ratio can be proved to be minimax ratio only under the assumption of lda classifier with gaussian distributions. the notion of minimax sampling is recently developed for .

  • f1 score

    in statistical analysis of binary classification, the f 1 score also f-score or f-measure is a measure of a test's considers both the precision p and the recall r of the test to compute the score: p is the number of correct positive results divided by the number of all positive results returned by the classifier, and r is the number of correct positive results divided by the .

  • music algorithm

    however, although the performance advantages of music are substantial, they are achieved at a cost in computation searching over parameter space and storage of array calibration data . application to frequency estimation. music estimates the frequency content of a signal or autocorrelation matrix using an eigenspace method.

  • cascading classifiers

    cascading classifiers are trained with several hundred 'positive' sample views of a particular object and arbitrary 'negative' images of the same size. after the classifier is trained it can be applied to a region of an image and detect the object in question. to search for the object in the entire frame, the search window can be moved across .

  • talk:high-performance sailing

    high-performance sailing is within the scope of the wikiproject sailing, a collaborative effort to improve wikipedia's coverage of sailing.if you would like to participate, you can visit the project page, where you can join the project and see a list of open tasks. start this article has been rated as start-class on the project's quality scale. mid

  • k-nearest neighbors algorithm

    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: . in k-nn classification, the output is a class membership.

  • list of algorithms

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  • mineral processing

    the high tension rollers are usually used for streams that have coarse conductors and fine non-conductors. these separators are commonly used for separating mineral sands , an example of one of these mineral processing plants is the crl processing plant at pinkenba in brisbane queensland.

  • expectation–maximization algorithm

    in statistics, an expectation–maximization em algorithm is an iterative method to find maximum likelihood or maximum a posteriori map estimates of parameters in statistical models, where the model depends on unobserved latent variables.the em iteration alternates between performing an expectation e step, which creates a function for the expectation of the log-likelihood evaluated using .

  • precision and recall

    balanced accuracy can serve as an overall performance metric for a model, whether or not the true labels are imbalanced in the data, assuming the cost of fn is the same as fp. another metric is the predicted positive condition rate ppcr , which identifies the percentage of the total population that is flagged. for example, for a search engine .

  • adaboost

    adaboost, short for adaptive boosting, is a machine learning meta-algorithm formulated by yoav freund and robert schapire, who won the 2003 gödel prize for their work. it can be used in conjunction with many other types of learning algorithms to improve performance. the output of the other learning algorithms 'weak learners' is combined into a weighted sum that represents the final output .

  • brier score

    the brier score is a proper score function that measures the accuracy of probabilistic predictions. it is applicable to tasks in which predictions must assign probabilities to a set of mutually exclusive discrete outcomes. the set of possible outcomes can be either binary or categorical in nature, and the probabilities assigned to this set of outcomes must sum to one where each individual .