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The FINDSalgorithm utilizes this general-to-specific ordering, performing a specific-to-general search through the hypothesis space along one branch of the partial ordering, to find the most specific hypothesis consistent with the training examples The CANDIDATE-ELIMINATION algorithm utilizes this general-to-specific ordering to compute the version space (the set of all hypotheses consistent with the training data) by incrementally computing the sets of maximally specific (S) and maximally general (G) hypotheses Because the S and G sets delimit the entire set of hypotheses consistent with the data, they provide the learner with a description of its uncertainty regarding the exact identity of the target concept This version space of alternative hypotheses can be examined to determine whether the learner has converged to the target concept, to determine when the training data are inconsistent, to generate informative queries to further refine the version space, and to determine which unseen instances can be unambiguously classified based on the partially learned concept Version spaces and the CANDIDATE-ELIMINATION algorithm provide a useful conceptual framework for studying concept learning However, this learning algorithm is not robust to noisy data or to situations in which the unknown target concept is not expressible in the provided hypothesis space 10 describes several concept learning algorithms based on the generalto-specific ordering, which are robust to noisy data Inductive learning algorithms are able to classify unseen examples only because of their implicit inductive bias for selecting one consistent hypothesis over another The bias associated with the CANDIDATE-ELIMINATION algorithm is that the target concept can be found in the provided hypothesis space (c E H) The output hypotheses and classifications of subsequent instances follow deductively from this assumption together with the observed training data If the hypothesis space is enriched to the point where there is a hypothesis corresponding to every possible subset of instances (the power set of the instances), this will remove any inductive bias from the CANDIDATEELIMINATION algorithm Unfortunately, this also removes the ability to classify any instance beyond the observed training examples An unbiased learner cannot make inductive leaps to classify unseen examples.

In this chapter, you learned how to build a JavaScript that can interact with child windows that are created when you insert a frameset into your web page A frameset is a parent frame that contains two or more smaller child frames inside Each small frame can be populated by a web page Although many frames are loaded with a static web page, you can dynamically build a web page within a frame by rst opening the child window and then using the write() method to write HTML tags directly to the child window The results are the same as loading a static web page; however, you can tailor the content based on activities that occur while your JavaScript application runs The content must be from the same domain as the web page; otherwise, a security violation occurs Along with dynamically building a web page within a frame, you also learned how to access and manipulate elements that appear in a child window, such as a button or input box displayed on a form The key to accessing these elements is to reference the completed path that begins with the parent and is followed by the child window and the form The parent is the frameset In 12, you'll learn how to interact with images using JavaScript

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The idea of concept learning and using the general-to-specific ordering have been studied for quite some time Bruner et al (1957) provided an early study of concept learning in humans, and Hunt and Hovland (1963) an early effort to automate it Winston's (1970) widely known PhD dissertation cast concept learning as a search involving generalization and specialization operators Plotkin (1970, 1971) provided an early formalization of the more-general-than relation, as well as the related notion of 8-subsumption (discussed in 10) Simon and Lea (1973) give an early account of learning as search through a hypothesis

The SectionList stored procedure returns a list of all the sections in a chapter that are displayed on the Sections page CREATE PROCEDURE SectionList @ID integer AS Declare @AllOfIt varchar(8000), @Name varchar(50), @SectionID varchar(10),

1 True or False A frameset can be loaded with a static web page a True b False 2 What attribute is used to specify the web page that is loaded into a frame a source b src c topPage d bottomPage 3 How can you hide the borders of a frame a frameborder="0" b toPageborder="0" bottomPageborder="0" c borders=hide d Borders cannot be hidden 4 What attributes can be used to change the source of a child window from a JavaScript a source b src c parentframelocationsource d parentframelocationhref 5 What frame receives focus by default a First frame that is built b Last frame that is built c No frame has focus d None of the above 6 How do you set the number of frames that appear in a frameset a Set the rows and cols values b Set the frame value c Set the frameset value d Set the child window value

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7 True or False You specify the name of the frame whenever you want to reference the contents of the frame a True b False 8 True or False A child window cannot change the content of another child window if they are on different domains a True b False 9 True or False A child window cannot call JavaScript functions that are de ned in another child window if they are on different domains a True b False 10 If you have two vertical frames, how do you make one frame smaller than the other frame a Make one of the rows values smaller than the other b Make one of the cols values smaller than the other c Make one of the bar values smaller than the other d Make one of the bar values larger than the other

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