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The word embedding approach is able to capture multiple different degrees of similarity between words. Mikolov et al. (2013) [ 26 ] found that semantic and syntactic patterns can be reproduced using vector arithmetic.
For instance, consider "SELECT name from Student". In the case that there are multiple records with name "Sara" in the student table, all of them are shown. That means the result of an SQL query is a multiset; if the result were instead a set, the repetitive records in the result set would have been eliminated.
A simple and inefficient way to see where one string occurs inside another is to check at each index, one by one. First, we see if there is a copy of the needle starting at the first character of the haystack; if not, we look to see if there's a copy of the needle starting at the second character of the haystack, and so forth.
Instead of receiving a set of instances which are individually labeled, the learner receives a set of labeled bags, each containing many instances. In the simple case of multiple-instance binary classification, a bag may be labeled negative if all the instances in it are negative. On the other hand, a bag is labeled positive if there is at ...
In other words, it is easier to think of words that begin with "K", more than words with "K" as the third letter. Thus, people judge words beginning with a "K" to be a more common occurrence. In reality, however, a typical text contains twice as many words that have "K" as the third letter than "K" as the first letter. [8]
One of the earliest content-based image retrieval systems to address the semantic problem was the ImageScape search engine. In this system, the user could make direct queries for multiple visual objects such as sky, trees, water, etc. using spatially positioned icons in a WWW index containing more than ten million images and videos using keyframes.
Polyinstantiation in computer science is the concept of type (class, database row or otherwise) being instantiated into multiple independent instances (objects, copies). It may also indicate, such as in the case of database polyinstantiation, that two different instances have the same name (identifier, primary key).
It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a classifier. [1] It has also been used for computer vision. [2]