A high-performance method was developed for protein secondary structure prediction based on the dual-layer support vector machine (SVM) and position-specific scoring matrices (PSSMs). SVM is a new machine learning technology that has been successfully applied in solving problems in the field of bioinformatics. The SVM's performance is usually better than that of traditional machine learning approaches. The performance was further improved by combining PSSM profiles with the SVM analysis. The PSSMs were generated from PSI-BLAST profiles, which contain important evolution information. The final prediction results were generated from the second SVM layer output. On the CB513 data set, the three-state overall per-residue accuracy, Q3, reached 75.2%, while segment overlap (SOV) accuracy increased to 80.0%. On the CB396 data set, the Q3 of our method reached 74.0% and the SOV reached 78.1%. A web server utilizing the method has been constructed and is available at http://www.bioinfo.tsinghua.edu.cn/pmsvm.

This paper studies the minimizing risk problems in Markov decision processes with countable state space and reward set. The objective is to find a policy which Ž . minimizes the probability risk that the total discounted rewards do not exceed a Ž . specified value target . In this sort of model, the decision made by the decision maker depends not only on system's states, but also on his target values. By introducing the decision-maker's state, we formulate a framework for minimizing risk models. The policies discussed depend on target values and the rewards may be arbitrary real numbers. For the finite horizon model, the main results obtained Ž . Ž . are: i The optimal value functions are distribution functions of the target, ii Ž . there exists an optimal deterministic Markov policy, and iii a policy is optimal if and only if at each realizable state it always takes optimal action. In addition, we obtain a sufficient condition and a necessary condition for the existence of finite horizon optimal policy independent of targets and we give an algorithm computing finite horizon optimal policies and optimal value functions. For an infinite horizon model, we establish the optimality equation and we obtain the structure property of optimal policy. We prove that the optimal value function is a distribution function of target and we present a new approximation formula which is the generalization of the nonnegative rewards cases. An example which illustrates the mistakes of previous literature shows that the existence of optimal policy has not been proved really. In this paper, we give an existence condition, which is a sufficient and necessary condition for the existence of an infinite horizon optimal policy independent of targets, and we point out that whether there exists an optimal policy remains an open problem in the general case.

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Abstract-We consider a problem of optimal control of a "retirement investment fund" over a finite time horizon with a target hitting time criteria. That is, we wish to decide, at each stage, what percentage of the current retirement fund to allocate into the limited number of investment options so that a decision maker can maximize the probability that his or her wealth exceeds a target prior to his or her retirement. We use Markov decision processes with probability criteria to model this problem and give an example based on data from certain options available in an Australian retirement fund.

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