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TANGENTPROP (Simard et al 1992) accommodates domain knowledge expressed as derivatives of the target function with respect to transformations of its inputs Consider a learning task involving an instance space X and target function f Up f to now we have assumed that each training example consists of a pair (xi, (xi)) that describes some instance xi and its training value f (xi)The TANGENTPROP algorithm assumes various training derivatives of the target function are also provided For example, if each instance xi is described by a single real value, then each training example may be of the form (xi, (xi), f lx, ) Here lx, denotes the derivative of the target function f with respect to x, evaluated at the point x = xi To develop an intuition for the benefits of providing training derivatives as well as training values during learning, consider the simple learning task depicted in Figure 125 The task is to learn the target function f shown in the leftmost plot f of the figure, based on the three training examples shown: (xl, (xl)),(x2,f (x2)), and (xg,f (xg)) Given these three training examples, the BACKPROPAGATION algorithm can be expected to hypothesize a smooth function, such as the function g depicted in the middle plot of the figure The rightmost plot shows the effect of

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As we just saw, Equation (72) provides a general basis for bounding the sample complexity for learning target concepts in some given class C Above we applied it to the class of conjunctions of boolean literals It can also be used to show that many other concept classes have polynomial sample complexity (eg, see Exercise 72)

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Not all concept classes have polynomially bounded sample complexity according to the bound of Equation (72) For example, consider the unbiased concept class

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If you ever obtain a permanent position programming the iPhone, this chapter covered material you will use everyday Trust me, after 15 years of programming, I know Doesn t matter if it s Java, Visual Basic, ColdFusion, ASP, or Objective-C if you are programming for the man, then you are probably moving data from point A to point B Forget interesting programming problems; reading, writing, updating, and deleting values from a database probably accounts for at least 80 percent of all code I have ever written Translation: You must understand database manipulation to get a job, so if you skipped this chapter s details, you should reconsider and go over the chapter again SQLite is the database chosen by Apple for iPhone programming, so you should know SQLite if you wish to program the iPhone

C that contains every teachable concept relative to X The set C of all definable target concepts corresponds to the power set of X-the set of all subsets of Xwhich contains ICI = 2IXI concepts Suppose that instances in X are defined by n boolean features In this case, there will be 11 = 2" distinct instances, and x therefore ICI = 2' = 2 distinct concepts Of course to learn such an unbiased 1 ' concept class, the learner must itself use an unbiased hypothesis space H = C Substituting I H I = 22ninto Equation (72) gives the sample complexity for learning the unbiased concept class relative to X

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Understanding Core Data s basics Creating a Core Data model Understanding how to load, fetch, and save a model s data Knowing where to obtain more Core Data information

Thus, this unbiased class of target concepts has exponential sample complexity under the PAC model, according to Equation (72) Although Equations (72) and (75) are not tight upper bounds, it can in fact be proven that the sample complexity for the unbiased concept class is exponential in n

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