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There is one problem: AAA clears the high nybble to 0 This means that adding '1' and '1' doesn't quite equal '2,' the displayable digit Instead, AL becomes 02, which in ASCII is the dark "smiley face" character To make AL a isplayable ASCII digit again, we have to add 30H to AL This is easy to do: Just add '0' to AL, which has a numeric value of 30H So, adding '0' takes 02H back up to 32H, which is the numeric equivalent of the ASCII digit character '2' This is the reason for the ADD AL,'0' instruction that immediately follows AAA There's a lot more to BCD math than what I've explained here When you want to perform multiplecolumn BCD math, you have to take carries into account, which involves a new flag called the Auxiliary carry flag AF There are also the AAD, AAM, and AAS instructions for adjusting AL after BCD divides, multiplications, and subtractions, respectively The same general idea applies: All the BCD adjustment instructions force the standard binary arithmetic instructions to come out right for BCD operands And yet another problem: AAA increments AH whenever it finds a value in the low nybble of AL greater than 9 In Ruler, AH contains the text attribute we're using to display our ruler, and if AH is incremented, the attribute will change and we'll end up displaying parts of the ruler in different colors This is why we have to do one last adjustment to AAA's adjustments: We reassert our desired text attribute in AH each time we change the ASCII digit in AL An interesting thing to do is comment out the ADD AL,'0' instruction in the Ruler macro and then run the RULERASM test program Another interesting thing to do (especially if you work on a color screen, as you almost certainly do) is to comment out the MOV AH,07 instruction in Ruler and then run RULERASM Details count, big time!.

too many free parameters may actually memorize training patterns and may also accurately t the noise embedded in the training data, leading to bad generalization Over tting can thus be prevented by reducing the size of the network through elimination of individual weights or units The objective is therefore to balance the complexity of the network with goodness-of- t of the true function This process is referred to as architecture selection Several approaches have been developed to select the optimal architecture, ie regularization, network construction (growing) and pruning These approaches will be overviewed in more detail below Learning is not just perceived as nding the optimal weight values, but also nding the optimal architecture However, it is not always obvious what is the best architecture Finding the ultimate best architecture requires a search of all possible architectures For large networks an exhaustive search is prohibitive, since the search space consists of 2w architectures, where w is the total number of weights [602] Instead, heuristics are used to reduce the search space A simple method is to train a few networks of di erent architecture and to choose the one that results in the lowest generalization error as estimated from the generalized prediction error [603, 604] or the network information criterion [616, 617, 618] This approach is still expensive and requires many architectures to be investigated to reduce the possibility that the optimal model is not found The NN architecture can alternatively be optimized by trial and error n architecture is selected, and its performance is evaluated If the performance is unacceptable, a di erent architecture is selected This process continues until an architecture is found that produces an acceptable generalization error Other approaches to architecture selection are divided into three categories: Regularization: Neural network regularization involves the addition of a penalty term to the objective function to be minimized In this case the objective function changes to (726) E = ET + EC where ET is the usual measure of data mis t, and EC is a penalty term, penalizing network complexity (network size) The constant controls the in uence of the penalty term With the changed objective function, the NN now tries to nd a locally optimal trade-o between data-mis t and network complexity Neural network regularization has been studied rigorously by Girosi et al [318], and Williams [910] Several penalty terms have been developed to reduce network size automatically 2 wi , is intended to drive small during training Weight decay, where EC = 1 2 weights to zero [79, 346, 435, 491] It is a simple method to implement, but su ers from penalizing large weights at the same rate as small weights To solve this problem, Hanson and Pratt [346] propose the hyperbolic and exponential penalty functions which penalize small weights more than large weights Nowlan and Hinton [633] developed a more complicated soft weight sharing, where the distribution of weight values is modeled as a mixture of multiple Gaussian distributions A narrow Gaussian is responsible for small weights, while a broad Gaussian is responsible for large weights Using this scheme, there is less pressure on large weights to be reduced Weigend et al [895] propose weight elimination where the penalty function.

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I've created a small demo program to show you hat I mean It's not as useful a tool as the Ruler macro, but it makes its point and is easy to understand The SHOWCHARASM program clears the screen and shows a table containing all 256 ASCII characters, neatly displayed in four lines of 64 characters each The table includes the "undisplayable" ASCII characters corresponding to the control characters whose values are less than 32 They are displayable from SHOWCHAR because the program writes them directly into video memory Neither DOS nor BIOS are aware of the display of the control characters, so they have no opportunity to interpret or filter out those characters with special meanings SHOWCHARASM introduces a number of new concepts and instructions, all related to program loops (String instructions such as STOSW and program loops are intimately related) Read over the main body of the SHOWCHARASM program carefully We go over it idea by idea through the next several pages ; ; ; ; ; ; ; ; ; ; ; Source name Executable name Code model: Version Created date Last update Author Description : : : : : : : : SHOWCHARASM SHOWCHARCOM Real mode flat model 10 9/18/1999 9/18/1999 Jeff Duntemann A simple example of a DOS COM file programmed for real mode flat model, using NASM 098 and ALINK This program demonstrates how multi-line macros are used with NASM ; Set 16 bit code generation ; Set code start address to 100h (COM file) ; Section containing code ; Load in screen control macro library ; This is where program execution begins: Clear VidOrigin,0720H,4000 ; Clear full video buffer to spaces ; Show a 64-character rule above the table display: Ruler VidOrigin,LineLen,ScrnWidth,0,.

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