Unfortunately, we are not allowed to cancel bets as soon as they have been placed. A guess is considered to be positioned as quickly as it is displayed in your betting account under “My bets”. Before you affirm a guess, you'll find a way to cancel it within the bet slip and never place the wager. However, upon getting confirmed the guess within the coupon your guess is accepted and might no longer be canceled. The technique according to claim 16, further comprising calculating the similarity between the Average Document Vector and each of the Prediction Vectors and offering a similarity worth associated with each text prediction. A doc entered by the person can then be added to all or none of the Document Delimited Text Sources 4, or only the most related Document Delimited Text Source 4.
The ‘documents’ are sections of textual content that are internally homogeneous with respect to some aspect of their content material (e.g. an article on a particular topic, or an e mail despatched to a particular person). The predictor 1 is educated using the textual knowledge contained within the Document Delimited Text Source 4. In these methods, completions are ordered on the idea of usage frequency statistics and in some instances (e.g. eZiText, AdapTxt, TouchPal) using instant lexical context. This provide only applies to a customer’s first account and gained't apply to any subsequently opened accounts.
The technique additional contains producing 25, using a Cosine Similarity Module 10, similarity values eleven for the predictions by determining the cosine similarity between the Average Document Vector 9 and every of the Prediction Vectors 8. The methodology further comprises Modifying 26, using a Weighting Module 12, the chances related to each textual content prediction using the similarity values. Finally, the strategy includes reordering 27, using the Vector-Space Similarity Model 5, the text predictions three and outputting the reordered textual content predictions 6 for display to a consumer of an digital device, and subsequent choice for entry into the digital gadget. In accordance with the current invention there is supplied a system and methodology which utilises a vector house approach, Random Indexing, to estimate the likelihood that a given time period or phrase belongs inside the present textual context.
The comparability can be jeopardised if the dimensions of the estimates is significantly modified. As said above, the modified chances for the textual content prediction parts are utilized by the system to reorder the textual content prediction components that have been generated by the system from consumer inputted text. The current invention represents a major enhancement over methods during which textual content predictions are ordered solely on the premise of recency or frequency. It permits the ordering of predictions to be influenced by the likelihood that the predicted time period or phrase belongs within the present contextual context, i.e. in the present textual content sequence entered by a consumer. The system in accordance with the current invention permits ‘nonlocal’ context to be taken into consideration. The methodology in accordance with declare 11, further comprising generating a set of Prediction Vectors, by retrieving from the vector map a context vector for every textual content prediction that has an equal in the vector map.
The value given is a total predicted for the earlier three hrs and includes the time of the forecast being checked out. The day label given represents the local day relative to the native time for the placement you're looking at. The textual content source used to train the predictor 1 of the system needn't be the Document Delimited Text Source four. However, for optimum results, the Document Delimited Text Source 4 is used to coach the predictor 1. The system of the invention includes additionally a Document Delimited Text Source 4, which is a set of textual data organised into ‘documents’.
It could be anticipated that if two phrases have occurred in exactly the same set of paperwork inside a set of training information, they want to be ‘close’ in the vector house. Conversely, if phrases have occurred in disjoint units of paperwork then they need to be ‘distant’ within the vector house. The methodology in accordance with declare 19, further comprising updating the vector map by assigning a model new index vector to the finished textual content sequence and
To read more about แทงบอลออนไลน์ visit เว็บแทงบอลby including the new index vector to the sum of index vectors for each term contained within the completed text sequence. The system in accordance with claim 2, whereby the processor is configured to update the vector map by assigning a brand new index vector to a completed textual content sequence enter and by including the brand new index vector to the sum of index vectors for each time period contained within the completed textual content sequence input. Once the e-mail has been completed by the user, this email is added to the Document Delimiting Text Source four, which is used to coach additional the predictor 1. Furthermore, the email is assigned a model new index vector which is then added to the context vectors for all terms contained in that document to update the Indexing Term-Vector Map 7.
The current invention relates usually to a system and methodology for the reordering of textual content predictions. More notably, the system and methodology reorders the text predictions based on modified probability values, wherein the likelihood values are modified according to the likelihood that a given textual content prediction will occur within the text inputted by a consumer. By way of a non-limiting example, if the person has inputted a doc into the system, the document is added to the Document Delimited Text Source 4 and the Random Indexing Term Vector Map 7 is updated to incorporate this doc. However, this doc is not essentially used to coach additional the predictor 1, and does not necessarily need to be added to the text sources similar to the multiple predictors of the predictor 1 of FIG. In this state of affairs, if a consumer have been to begin entering the identical word sequence as that of the beforehand entered document, the predictions generated by the predictor 1 could be the identical as those generated for the beforehand entered document . The output from the Weighting Module 12 is a set of reordered predictions 6.
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