We derive two-point step sizes for the steepest-descent method by approximating the secant equation. At the cost of storage of an extra iterate and gradient, these algorithms achieve better performance and cheaper computation than the classical steepest-descent method. We indicate a convergence analysis of the method in the two-dimensional quadratic case.
The behaviour is highly remarkable and the analysis entirely nonstandard. Most users should sign in with their email address. If you originally registered with a username please use that to sign in. To purchase short term access, please sign in to your Oxford Academic account above.
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Select Format Select format. Permissions Icon Permissions. Abstract We derive two-point step sizes for the steepest-descent method by approximating the secant equation.
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Don't have an account? Sign in via your Institution Sign in. Purchase Subscription prices and ordering Short-term Access To purchase short term access, please sign in to your Oxford Academic account above. This article is also available for rental through DeepDyve.Business Insider reports that roughly 310 of the nation's 1,300 shopping malls are at risk of losing a so-called anchor store, citing data from commercial real estate firm CoStar.
Malls struggle to replace these anchor stores, which typically take up prime real estate. Stores like Gap and Nordstrom will thrive by becoming destinations that offer more than just clothing. Flashy gimmicks and personalized service will turn shopping from a casual pastime into an anticipated event. Bezos missed the mark on this one, but he's not far off.
Although retail is suffering big time, it's not for lack of trying. As retailers struggle to attract customers, they admit that making the in-store experience more exciting is crucial, but few have cracked how to do so successfully, CNBC reports.
But as retail sales continue to slip, it seems they'll have to keep pushing for a more complete breakthrough. And according to Business Insider, movie theaters aren't doing so well either.
As Bezos succinctly told Wired: "Strip malls are history. Download the latest Flash player and try again. Playing Share this video. Please upgrade to watch video. The requested video is unable to play. The video does not exist in the system.
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Login Register Competing against other forecasters, you simply find a question you are interested in, make a forecast and add your reasoning for extra kudos. There is something for everyone, from politics to finance, economics to technology. Not a penny, just some of your time and knowledge. But while the gains are in line with action seen in most of the market, social media suggests XRP may be gaining on bullish statements. Making the rounds is one report by Palm Beach Research Group, which suggests the analyst expects the cryptocurrency to outperform in 2018.
Trading volumes on Bithumb, one of the largest exchanges in South Korea, have gone up by 45 percent in the last 24 hours.
That said, the volume pop was short lived as market attention shifted back to bitcoin. Still, the price action analysis in XRP favors the bulls. Interested in offering your expertise or insights to our reporting. Disclaimer: This article should not be taken as, and is not intended to provide, investment advice. Please conduct your own thorough research before investing in any cryptocurrency. What will be the hottest sector of blockchain this fall.
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Reviews from customers are an important part of the website content strategy for any business. Customer reviews guide and assist customers in making buying decisions. Marketing experts suggest to incorporate customer reviews throughout your web pages, on places where they could make a difference: on your homepage or next to your services.
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When a customer writes a review, you are immediately notified in your Bookeo Home Page. You can manage your reviews and publish them. You can:select the reviews that you want to publish select the "Summary". It is the only part of the review shown in your booking page, and the preview in your Customer Reviews Widget select the customer details to be published you can publish Customer Reviews at the bottom of your booking pages (remember, only the "Summary" will be shown) you can also publish Customer Reviews in any page of your website using the Customer Reviews Widget.
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Customers will be able to scroll through your published reviews. You can customize the colours, size and language of the widget.The use of non-parametric methods may be necessary when data have a ranking but no clear numerical interpretation, such as when assessing preferences. In terms of levels of measurement, non-parametric methods result in "ordinal" data. As non-parametric methods make fewer assumptions, their applicability is much wider than the corresponding parametric methods.
In particular, they may be applied in situations where less is known about the application in question. Also, due to the reliance on fewer assumptions, non-parametric methods are more robust. Another justification for the use of non-parametric methods is simplicity. In certain cases, even when the use of parametric methods is justified, non-parametric methods may be easier to use. Due both to this simplicity and to their greater robustness, non-parametric methods are seen by some statisticians as leaving less room for improper use and misunderstanding.
Mathematical statistics has substantial overlap with the discipline of statistics. Statistical theorists study and improve statistical procedures with mathematics, and statistical research often raises mathematical questions.
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Theory of Point Estimation (2nd ed. Mathematical Statistics: Basic and Selected Topics. Asymptotic Methods in Statistical Decision Theory. Statistical Decision Theory: Estimation, Testing, and Selection.
Anniversary statistical collection World Statistics Day 2015 BRICS Joint Statistical Publications 39, Miasnitskaya St.For classification ensembles, the combination is made by majority vote. The options are: 0: plurality weights each model's prediction as one vote. You can set up both using the threshold argument. If there are less than k models voting class, the most frequent of the remaining categories is chosen, as in a plurality combination after removing the models that were voting for class.
The confidence of the prediction is computed as that of a plurality vote, excluding votes for the majority class when it's not selected. For regression ensembles, the predicted values are averaged.
For a logistic regression, input data for all numerical fields except the objective field must be provided. Example: "my new prediction" private optional Whether you want your prediction to be private or not. This will be 201 upon successful creation of the prediction and 200 afterwards.
Make sure that you check the code that comes with the status attribute to make sure that the prediction creation has been completed without errors.
The method used to combine predictions from the non-boosted ensemble. See the available combiners above. However, for logistic regressions, it really means probability, and thus, confidence will be deprecated soon. Note that this property is not available for ensembles with boosted trees and that for models An array of confidence pairs for each category in the objective field.
True when the prediction has been created in the development mode. The number of predictions in the ensemble that failed. The dictionary of input fields' ids and values used as input for the prediction.
Specifies the type of strategy that a model or models in an ensemble will follow when a missing value needed to continue with inference in the model is found. Either 0, 1, or 2 to specify respectively whether the prediction is from a single model, an ensemble, or a logistic regression.
The id of the field that it predicts in the model, ensemble, or logistic regression. A string if the task is classification, a number if the task is regression prediction filterable, sortable A dictionary keyed with the objective field to get the prediction output for the model, ensemble, or logistic regression.
An array with a prediction object for each model in the non-boosted ensemble. An array of probability pairs for each category in the objective field. The parameters (k and class) given when a threshold-based combiner is used for the non-boosted ensemble.
A list of the confidence (or expected error in the regression non-boosted ensemble) for each prediction candidate. Bad fields are ignored. That is, if you submit a value that is wrong, a prediction is created anyway ignoring the input field with the wrong value.
An ordered array of Predicate Objects in the decision path from the root to the current node or to a final decision if the the next predicate array is empty. Unknown fields are ignored. That is, if you submit a field that is wrong, a prediction is created anyway ignoring the wrong input field.
An array of field's ids with wrong values submitted to build the model or logistic regression. A status code that reflects the status of the prediction creation. Example: "my new centroid" private optional Whether you want your centroid to be private or not. A dictionary describing the centroid.Example: "Prediction" probabilities optional Boolean,default is false Whether to include the predicted class and all other possible class values for the batch prediction for the classification task.
If enabled, the columns are included after the confidence score. Example: true probability optional Boolean,default is false Whether the probability for each prediction for the classification task should be added.
It's 1 by default. This is the usual default in some systems trying to detect anomalies (e. IDS and the like), and other uses of this combiner should probably not rely on our default value.
Their use is deprecated, and maintained only for backwards compatibility. Example: true You can also use curl to customize a new batch prediction. For example, to create a new batch prediction named "my batch prediction", that will not include a header, and will only output the field "000001" together with the confidence for each prediction. Once a batch prediction has been successfully created it will have the following properties.
Creating a batch prediction is a process that can take just a few seconds or a few hours depending on the size of the dataset used as input and on the workload of BigML's systems. The batch prediction goes through a number of states until its finished.
Through the status field in the batch prediction you can determine when it has been fully processed. Once you delete a batch prediction, it is permanently deleted. If you try to delete a batch prediction a second time, or a batch prediction that does not exist, you will receive a "404 not found" response.
However, if you try to delete a batch prediction that is being used at the moment, then BigML. To list all the batch predictions, you can use the batchprediction base URL. By default, only the 20 most recent batch predictions will be returned.
You can get your list of batch predictions directly in your browser using your own username and API key with the following links. You can also paginate, filter, and order your batch predictions. Batch Centroids Last Updated: Monday, 2017-10-30 10:31 A batch centroid provides an easy way to compute a centroid for each instance in a dataset in only one request. Batch centroids are created asynchronously. You can also list all of your batch centroids.
You can easily create a new batch centroid using curl as follows. All the fields in the dataset Specifies the fields in the dataset to be considered to create the batch centroid. Example: "my new batch centroid" newline optional String,default is "LF" The new line character that you want to get as line break in the generated csv file: "LF", "CRLF".
For example, to create a new batch centroid named "my batch centroid", that will not include a header, and will only ouput the field "000001" together with the distance for each centroid. Once a batch centroid has been successfully created it will have the following properties.
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