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An application running in Mac OS X on a notebook or desktop Mac has a disaggregated design: that is, the program is presented on the screen in one or more windows and the input is handled via the keyboard and mouse. The iPhone, by contrast, has everything in the same place: the display is the user interface. If you want to click a button you push it directly, and if you want to move a slider you put your finger directly on it and slide it. The only exception to this is where you have text- or number-entry fields, since you use

FIGURE 72 Exhausting the version space The version space VSH,Dis the subset of hypotheses h E H, which have zero training error (denoted by r = 0 in the figure) Of course the true errorv(h) (denoted by error in the figure) may be nonzero, even for hypotheses that commit zero errors over the training ~ data The version space is said to be -exhausted when all hypotheses h remaining in V S H ,have errorw(h) < E

Developed cost figures for construction and maintenance of facilities Prepared drawings, specifications, and related construction documents for facilities

are drawn Haussler (1988) provides such a bound, in the form of the following theorem Theorem 71 -exhaustingthe version space If the hypothesis space H is finite, and D is a sequence of rn 1 independent randomly drawn examples of some target concept c, then for any 0 5 E 5 1, the probability that the version space V S H ,is ~ not -exhausted (with respect to c) is less than or equal to

a virtual keyboard to do this (and to me this is the only slightly jarring usability note in using the iPhone). The consequence for you as a developer is that you need to be aware of the shortcomings of the virtual keyboard and avoid asking the user to type too much. Apart from that, the direct-manipulation features offer great usability benefits, providing a very natural mechanism to manipulate controls, together with excellent feedback.

xi) F f (xi)

Of course there will, in general, be many different hypotheses h that satisfy (V(X~, f (xi)) E D ) (B A h A xi) F f (xi) One common heuristic in ILP for choosing among such hypotheses is to rely on the heuristic known as the Minimum Description Length principle (see Section 66) There are several attractive features to formulating the learning task as finding a hypothesis h that solves the relation (V(xi,f (xi))E D ) (B A h A xi) F f (xi)

Despite the availability of rich gesture-based input mechanisms such as the multitouch trackpad on recent notebooks and the Magic Mouse, this is a major area of difference between Mac OS X applications and iPhone applications. The iPhone is both more limited (keyboard-type inputs using stubby (at least in my case) fingers can be a little clumsy) and more sophisticated (with the ability to slide, spin, pinch, and expand objects using gestures). As you will know from the enormous variety of applications on the App Store, the availability of a direct-manipulation, gesture-driven interface has released a huge wave of creativity in developers. The usability of the resulting software varies from the incredibly intuitive to the, frankly, obscure. Looking at how other developers have implemented their solutions for the iPhone can be very instructive when you are designing your own.

This formulation subsumes the common definition of learning as finding some general concept that matches a given set of training examples (which corresponds to the special case where no background knowledge B is available) By incorporating the notion of background information B, this formulation allows a more rich definition of when a hypothesis may be said to "fit" the data Up until now, we have always determined whether a hypothesis

(eg, neural network) fits the data based solely on the description of the hypothesis and data, independent of the task domain under study In contrast, this formulation allows the domain-specific background information B to become part of the definition of "fit" In particular, h fits the training example (xi, f (xi)) as long as f (xi) follows deductively from B A h A xi By incorporating background information B, this formulation invites learning methods that use this background information to guide the search for h, rather than merely searching the space of syntactically legal hypotheses The inverse resolution procedure described in the following section uses background knowledge in this fashion