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(A number of variants can be created by permutations and/or sign changes of the letters.) There is an obvious resemblance to the formula for two-square products, but once again there is no indication as to why this relationship exists in the first place, though it is perhaps a hint that there is no similar formula for the product of sums of the three squares. Turning to our other connection, Hamilton (so the story goes) was standing on a bridge near Dublin, when the solution to a problem that had been troubling him for fifteen years came to him in a moment of inspiration. When multiplying two algebraic quantities together, it is not necessary that A x B should always equal B x A. Of course they do in traditional algebra, but Hamilton was not thinking of traditional algebra: far from it, he was thinking of a kind of hypercomplex algebra in which each 'number' was composed of four components, three of which were like the 'imaginary' component of a complex number. Hamilton's new numbers looked like this:
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where the superscripts T and H , respectively, denote the transpose and complex conjugate transpose of a vector or matrix. Let R define the L-by-L correlation matrix of the signal received by the L elements:
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1 = l ,. . . , L
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FIGURE 4.28 The Options tab of the audio device Properties dialog box
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Security Groups/Built-in Domain Global (Can Be Added to Domain Local) Domain Admins Domain Guests Domain Users Group Policy Creator Owners Designated administrators of the domain (members of this group are de facto all Administrators). All domain guests. All domain users. Members in this group can modify Group Policy for the domain.
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Naive Bayes is a very fast algorithm with accuracy that is adequate for many applications. It does not, however, operate on continuous variables. The Naive portion of the name derives from this algorithm s assumption that every input is independent. For example, the probability of a married person purchasing a bike is computed from how often married and bike buyer appear together in the training data without considering any other columns. The probability of a new case is just the normalized product of the individual probabilities. Several viewers display data from the finished model: Dependency Network: Displays both input and predictable columns as nodes with arrows indicating what predicts what. Move the slider to the bottom to see only the most significant predictions. Click on a node to highlight its relationships. Attribute Profile: Similar in function to the Cluster Profile Viewer, this shows all variables and predictable outcomes in a single matrix. Each cell of the matrix is a graphical representation of that variable s distribution for a given outcome. Click on a cell (chart) to see the full distribution for that outcome/variable combination in the Mining Legend, or hover over a cell for the same information in a tooltip. Attribute Characteristics: This viewer displays the list of characteristics associated with the selected outcome. Attribute Discrimination: Similar to the Characteristics Viewer, but shows which characteristics favor one outcome versus another.
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