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With the increase in the size of real-world databases, there is an ever-increasing need to scale up inductive learning algorithms. Incremental learning techniques are one possible solution to the scalability problem. In this paper, we propose three criteria to evaluate the robustness and reliability of incremental learning methods, and use them to study the robustness of an incremental training method for Support Vector Machines. We provide empirical results using benchmark machine learning datasets to show that support vectors form a succinct and sucient set for block-by-block incremental learning. 1 Introduction When developing classiers using learning methods, more training data can reduce risk, but the learning process can get computationally intractable. This issue is becoming more evident with large amounts of data available [5]. Researchers in machine learning and data mining [2, 3, 7] have therefore been trying to scale up classical inductive learning algorithms to handle ex...

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Introduction This chapter gives an overview on multilevel transactions and its generalization toward open nested transactions. The main features of these transaction models are the following: first, semantic properties of operations can be exploited to relax the isolation of concurrent transactions; second, as a consequence, atomicity is achieved by compensation rather than state-based undo; and third, subtransactions can be made persistent independently of their commit state, that is, global visibility of their updates. Advanced transaction models and new correctness criteria for transaction executions have been proposed for the following reasons (and possibly further reasons that are not mentioned here): 1. to provide better support for long-lived activities in advanced DBMS applications, 2. to relax the classical ACID paradigm, for example, provide more flexibility as to when updates are made visible to concurrent transactions, 3. to support cooperation between the members

Message logging protocols are an integral part of a technique for implementing processes that can recover from crash failures. All message logging protocols require that, when recovery is complete, there be no orphan processes, which are surviving processes whose states are inconsistent with the recovered state of a crashed process. We give a precise specification of the consistency property "no orphan processes". From this specification, we describe how different existing classes of message logging protocols (namely optimistic, pessimistic, and a class that we call causal) implement this property. We then propose a set of metrics to evaluate the performance of message logging protocols, and characterize the protocols that are optimal with respect to these metrics. Finally, starting from a protocol that relies on causal delivery order, we show how to derive optimal causal protocols that tolerate f overlapping failures and recoveries for a parameter f : 1 f n. 1 Introduction Message ...

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An analysis of the efficiency of sourcing knowledge-based services in the United States Air Force

Background: Prospective studies on physically healthy subjects have shown an association between depression and the subsequent development of coronary heart disease (CHD). The relative risk in meta-analytic aggregation is 1.64 (confidence interval [CI], 1.29–2.08) for any CHD event. However, the adverse impact of depression on CHD patients has not yet been the subject of a meta-analysis. Objective: To quantify the impact of depressive symptoms (eg, BDI, HADS) or depressive disorders (major depression) on cardiac or all-cause mortality. We analyzed the strength of the relationship, the time dependency, and the differences in studies using depressive symptoms or a clinical diagnosis as predictors of mortality. Method: English and German language databases (Medline, PsycInfo, PSYNDEX) from 1980 to 2003 were searched for prospective cohort studies. Sixty-two publications were identified. The inclusion criteria were met by 29 publications reporting on 20 studies. A random model was used to estimate the combined overall effect as crude odds ratios (OR) or adjusted hazard ratios (HR [adj]). Results: Depressive symptoms increase the risk of mortality in CHD patients. The risk of depressed patients dying in the 2 years after the initial assessment is two times higher than that of nondepressed patients (OR, 2.24; 1.37–3.60). This negative prognostic effect also remains in the long-term (OR, 1.78; 1.12–2.83) and after adjustment for other risk factors (HR [adj], 1.76; 1.27–2.43). The unfavorable impact of depressive disorders was reported for the most part in the form of crude odds ratios. Within the first 6 months, depressive disorders were found to have no significant effect on mortality (OR, 2.07; CI, 0.82–5.26). However, after 2 years, the risk is more than two times higher for CHD patients with clinical depression (OR, 2.61; 1.53–4.47). Only three studies reported adjusted hazard ratios for clinical

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