rdlc data matrix

rdlc data matrix

rdlc data matrix

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rdlc data matrix

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In certain cases, the available data may be missing values for some attributes For example, in a medical domain in which we wish to predict patient outcome based on various laboratory tests, it may be that the lab test Blood-Test-Result is available only for a subset of the patients In such cases, it is common to estimate the missing attribute value based on other examples for which this attribute has a known value Consider the situation in which Gain(S, A ) is to be calculated at node n in the decision tree to evaluate whether the attribute A is the best attribute to test at this decision node Suppose that ( x ,c ( x ) ) is one of the training examples in S and that the value A ( x ) is unknown One strategy for dealing with the missing attribute value is to assign it the value that is most common among training examples at node n Alternatively, we might assign it the most common value among examples at node n that have the classification c ( x ) The elaborated training example using this estimated value for A(x) can then be used directly by the existing decision tree learning algorithm This strategy is examined by Mingers (1989a) A second, more complex procedure is to assign a probability to each of the possible values of A rather than simply assigning the most common value to A(x) These probabilities can be estimated again based on the observed frequencies of the various values for A among the examples at node n For example, given a boolean attribute A, if node n contains six known examples with A = 1 and four with A = 0, then we would say the probability that A ( x ) = 1 is 06, and the probability that A ( x ) = 0 is 04 A fractional 06 of instance x is now distributed down the branch for A = 1, and a fractional 04 of x down the other tree branch These fractional examples are used for the purpose of computing information Gain and can be further subdivided at subsequent branches of the tree if a second missing attribute value must be tested This same fractioning of examples can also be applied after learning, to classify new instances whose attribute values are unknown In this case, the classification of the new instance is simply the most probable classification, computed by summing the weights of the instance fragments classified in different ways at the leaf nodes of the tree This method for handling missing attribute values is used in C45 (Quinlan 1993).

Putting It All Together (Authoring)

Listing 7-8

Once all the assets are prepared and proofed, bring them into the authoring system and knit them all together A variety of authoring paradigms are available Some systems stick close to the DVD specification, while others try to hide terminology and details Most provide an onscreen flowchart, timeline, or combination of both Large production environments may benefit from networked authoring workstations, where each station focuses on a specific task (encoding, menu creation, asset integration, simulation, formatting, and so on) and the project data flows from station to station over a high-speed network At the authoring stage, specify basic parameters of the volume such as video format and disc size Import audio and video, synchronize them, and add chapter points Then, apportion titles and title sets Create menus by importing graphics and subpictures, link buttons together for directional highlighting, and link buttons to video segments or to other menus Those

[Session started at 2009-06-18 07:19:59 -0400] 2009-06-18 07:20:07503 SimpleView[267:20b] view did load 2009-06-18 07:20:10815 SimpleView[267:20b] Click Me 2009-06-18 07:20:15293 SimpleView[267:20b] Received simulated memory warning 2009-06-18 07:20:15294 SimpleView[267:20b] received memory warning 2009-06-18 07:20:33478 SimpleView[267:20b] view rotated

In some learning tasks the instance attributes may have associated costs For example, in learning to classify medical diseases we might describe patients in terms of attributes such as Temperature, BiopsyResult, Pulse, BloodTestResults, etc These attributes vary significantly in their costs, both in terms of monetary cost and cost to patient comfort In such tasks, we would prefer decision trees that use low-cost attributes where possible, relying on high-cost attributes only when needed to produce reliable classifications ID3 can be modified to take into account attribute costs by introducing a cost term into the attribute selection measure For example, we might divide the Gpin