976 resultados para VC
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We generalize the classical notion of VapnikChernovenkis (VC) dimension to ordinal VC-dimension, in the context of logical learning paradigms. Logical learning paradigms encompass the numerical learning paradigms commonly studied in Inductive Inference. A logical learning paradigm is defined as a set W of structures over some vocabulary, and a set D of first-order formulas that represent data. The sets of models of in W, where varies over D, generate a natural topology W over W. We show that if D is closed under boolean operators, then the notion of ordinal VC-dimension offers a perfect characterization for the problem of predicting the truth of the members of D in a member of W, with an ordinal bound on the number of mistakes. This shows that the notion of VC-dimension has a natural interpretation in Inductive Inference, when cast into a logical setting. We also study the relationships between predictive complexity, selective complexitya variation on predictive complexityand mind change complexity. The assumptions that D is closed under boolean operators and that W is compact often play a crucial role to establish connections between these concepts. We then consider a computable setting with effective versions of the complexity measures, and show that the equivalence between ordinal VC-dimension and predictive complexity fails. More precisely, we prove that the effective ordinal VC-dimension of a paradigm can be defined when all other effective notions of complexity are undefined. On a better note, when W is compact, all effective notions of complexity are defined, though they are not related as in the noncomputable version of the framework.
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Medical personnel serving with the Defence Forces have contributed to the evolution of trauma treatment and the advancement of prehospital care within the military environment. This paper investigates the stories of an Australian Medical Officer, Sir Neville Howse, and two stretcher bearers, Private John Simpson (Kirkpatrick) and Private Martin OMeara, In particular it describes the gruelling conditions under which they performed their roles, and reflects on the legacy that they have left behind in Australian society. While it is widely acknowledged that conflicts such as World War One should never have happened, as civilian and defence force paramedics, we should never forget the service and sacrifice of defence force medical personnel and their contribution to the body of knowledge on the treatment of trauma. These men and women bravely provided emergency care in the most harrowing conditions possible. However, men like Martin OMeara may not have been given the same status in society today as Sir Neville Howse or Simpson and his donkey, due to the publics lack of awareness and acceptance of war neurosis and conditions such as post traumatic stress disorder, reactive psychosis and somatoform disorders which were suffered by many soldiers during their wartime service and on their return home after fighting in war.
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In this paper, a rate-based flow control scheme based upon per-VC virtual queuing is proposed for the Available Bit Rate (ABR) service in ATM. In this scheme, each VC in a shared buffer is assigned a virtual queue, which is a counter. To achieve a specific kind of fairness, an appropriate scheduler is applied to the virtual queues. Each VC's bottleneck rate (fair share) is derived from its virtual cell departure rate. This approach of deriving a VC's fair share is simple and accurate. By controlling each VC with respect to its virtual queue and queue build-up in the shared buffer, network congestion is avoided. The principle of the control scheme is first illustrated by maxmin flow control, which is realised by scheduling the virtual queues in round-robin. Further application of the control scheme is demonstrated with the achievement of weighted fairness through weighted round robin scheduling. Simulation results show that with a simple computation, the proposed scheme achieves the desired fairness exactly and controls network congestion effectively.
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A high-level relationPopper dimension( Exclusion dimension( VC dimension( between Karl Poppers ideas on falsifiability of scientific theories and the notion of overfittingOverfitting in statistical learning theory can be easily traced. However, it was pointed out that at the level of technical details the two concepts are significantly different. One possible explanation that we suggest is that the process of falsification is an active process, whereas statistical learning theory is mainly concerned with supervised learningSupervised learning, which is a passive process of learning from examples arriving from a stationary distribution. We show that concepts that are closer (although still distant) to Karl Poppers definitions of falsifiability can be found in the domain of learning using membership queries, and derive relations between Poppers dimension, exclusion dimension, and the VC-dimensionVC dimension.
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