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In the preceding two chapters, you learned about the three levels of allocation asset classes, fund styles, and speci c funds. In this chapter I choose speci c mutual funds to ll out the allocations. I ve selected some of the funds I like and allocated them according to the four model portfolios to match the capitalization levels and styles we ve already discussed. You ll recall that the models are the conservative, the moderate, the aggressive, and the bunker that were discussed in 5. As of this writing, I believe the mutual funds I chose for the sample portfolios and their mutual fund managers are some of the best in the business. But this book is not about recommending the so-called best mutual funds or managers. I can t emphasize enough that you shouldn t conclude that these exact portfolios or mutual funds are ones that can t everyone s needs. I am only using them to illustrate the process you should use with an advisor to get to know your team. Why can t I prescribe the perfect fund to t your needs for the long haul Because there are too many variables that change all too fast. At any time a manager could leave or burn out. Or a fund could close. For example, years ago FPA Paramount, managed by Bill Sams, and Fidelity s Advisor Growth Opportunities fund, managed by George Vanderheiden, were two of my favorite funds. But over the years the funds performances slipped, and both managers eventually retired from their posts. I no longer use these funds.

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Funds versus Stocks Fund picking is hard, but stock picking is harder. Funds can be risky, but stocks are generally riskier. Funds have some paperwork, but stocks have more. Funds give small investors instant diversi cation that s more dif cult to achieve with stocks.

Traditional expert systems for fault diagnosis try to extract the knowledge of one or several experts to build a tool that can be used by non-specialists This allows newcomers to effectively analyse and solve the majority of faults Furthermore, the knowledge from different experts is stored in the tool in an organised manner, not in a diffuse set of rules that only a few people know and understand An expert system consists of two parts: a knowledge base and an inference engine The knowledge base contains the domain-speci c knowledge, which should be transferred from the experts to the system (eg knowledge about mobile networks TS, medical knowledge of diseases and their symptoms, etc) The inference engine performs reasoning to obtain conclusions based on the knowledge base and the domain observations.

Command:AREA Specify first corner point or [Object/Add/Subtract]:O Select objects; (pick 1) Area = 16.1434 Perimeter = 19.6824

In speeches, testimony, and the media, we continually hear about the need for tougher laws to combat identity theft. The fact is that a number of excellent federal statutes and acts have been in existence for many years. Both new legislation and enhanced versions of prior laws are always being enacted to add to the arsenal of government weapons. Statutes, or laws, are passed by legislatures or, in the case of federal statutes, by the United States Congress. These statutes include the old stalwarts of mail fraud, wire fraud, unauthorized use of a Social Security number (SSN), and access device fraud as well the newer aggravated identity theft statute. The states have also taken action by passing identity theft statutes mimicking federal criminal laws. The United States Congress has enacted legislation covering consumer credit, healthcare accounts, privacy, identification documents, money laundering, terrorism, and other important individual and business issues. All of these issues are impacted by identity theft, and these legislative acts and criminal statutes are intended to provide greater protections to individuals and businesses. A detailed summary of the significant federal criminal statutes and legislative acts related to the prevention and prosecution of identity theft related crimes as well as protections afforded consumers and businesses follows.

It is important to point out that these expert systems are actually decision support systems, which do not try to completely replace experts, but to support less experienced people 13531 Rule-based Systems The rst expert systems were based on rules Rules have the form: IF A THEN B , where A is an assertion and B can be an assertion or an action Some rules for TS could be: IF there are HW alarms THEN there is a HW fault or IF number of HO failures is too high THEN check whether calls are being handled by expected adjacencies The set of rules forms the knowledge base and the inference engine can select the rules relevant to the particular case under consideration and suggest further assertions or deductions In several cases, operators have owcharts describing their TS procedure, which can be directly converted into rules.