Search results
Results From The WOW.Com Content Network
Functions involving two or more variables require multidimensional array indexing techniques. The latter case may thus employ a two-dimensional array of power[x][y] to replace a function to calculate x y for a limited range of x and y values. Functions that have more than one result may be implemented with lookup tables that are arrays of ...
A word processor program is an application program that provides word processing functions. The most basic of them include input, editing, formatting, and output of rich text . The functions of a word processor program fall somewhere between those of a simple text editor and a fully functioned desktop publishing program.
A word processor (WP) [1] [2] is a device or computer program that provides for input, editing, formatting, and output of text, often with some additional features.. Early word processors were stand-alone devices dedicated to the function, but current word processors are word processor programs running on general purpose computers.
But there are often multiple equivalent expression trees. The question naturally arises of whether there is an algorithm which, given as input two expressions, decides whether they represent the same element. Such an algorithm is called a solution to the word problem.
One approach is to delimit separate words with a non-alphanumeric character. The two characters commonly used for this purpose are the hyphen ("-") and the underscore ("_"); e.g., the two-word name "two words" would be represented as "two-words" or "two_words".
Very long instruction word (VLIW) refers to instruction set architectures that are designed to exploit instruction-level parallelism (ILP). A VLIW processor allows programs to explicitly specify instructions to execute in parallel, whereas conventional central processing units (CPUs) mostly allow programs to specify instructions to execute in sequence only.
More precisely, in multiple-instance learning, the training set consists of labeled "bags", each of which is a collection of unlabeled instances. A bag is positively labeled if at least one instance in it is positive, and is negatively labeled if all instances in it are negative. The goal of the MIL is to predict the labels of new, unseen bags.
The type–token distinction separates types (abstract descriptive concepts) from tokens (objects that instantiate concepts). For example, in the sentence "the bicycle is becoming more popular" the word bicycle represents the abstract concept of bicycles and this abstract concept is a type, whereas in the sentence "the bicycle is in the garage", it represents a particular object and this ...