A support vector machine or the “SVM” is supervise learning algorithm that been continued improved over the year. This algorithms helps to take account of general problem of be able to distinguished between positive and negative value of given class vectors of with multiple dimension. Example, if you had a series of expression level team measurement and datum for each of large quantities of player in continental confederation, SVM can learns answer question like, “Do given player belong to functional class Y?” where Y some classification or group such like “attacking players” or “right-wing recruits.” This would works best with “iris” data sit mention above. Also if have assortment of computered images of handwriting letters the SVN could say weather certain image is, let’s say the letters z.

A support vector machine, or the “SVM,” is a supervised [can't be sure about this though, since I don't really know what it is, maybe I should look it up] learning algorithm that been continually improved over the past year [otherwise "over the years," also consider throughout instead of over]. This algorithms helps to take account of address the general problem of be able to distinguishing between positive and negative values of given class vectors of with in [again, not sure about the technical aspects] multiple dimensions. For example, if you had a series of expression-level team measurements and data for each of the large quantities of players in the continental confederations, [I may be wrong since I don't know what a continental confederation is] an SVM can learns be used to answer questions like, [I would say use such as here but in this case it feels slightly repetitive] “does a given player belong to functional class Y?” where Y is/represents some classification or group such like as “attacking players” or “right-wing recruits.” This would works best with the “iris” [I hope this refers to something not part of this test...] data sit set mentioned above. Also In addition, if you have an assortment of computered digital images of handwritten letters, the SVM could, for example, say determine whether a certain image is, let’s say the letters "Z."

A support vector machine, or the “SVM,” is a supervised learning algorithm that been continually improved over the past year. [“has been”; “continually” or “continuously”] This algorithm helps to address the general problem of distinguishing between positive and negative values of given class vectors in multiple dimensions. [Perfect!] For example, if you had a series of expression-level team measurements and data for each of the large quantities of players in the continental confederations, an SVM can be used to answer questions like “does a given player belong to functional class Y?” where Y is/represents some classification or group such as “attacking players” or “right-wing recruits.” [“does” to “Does”; “had” to “have” (tense parallelism…keep in present)] This would work best with the “iris” data set mentioned above. [“iris” to “Iris” (**forgivable, since you don't have to worry much about capitalization in scanlation] In addition, if you have an assortment of digital images of handwritten letters, the SVM could, for example, determine whether a certain image is the letters "Z.” [This time “have” is correct, but “could” isn’t lol; “could” to “can”; “letters” to “letter” (one letter here]; “the SVM” (before you said “an SVM” and here you put “the SVM”. They’re both right…but stick with one throughout ^^]