Directed evidential graphical models are important tools for handling uncertain information in the framework of evidence theory. they obtain their efficiency by compactly representing (in)dependencies between variable...
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the paper is addressed to economic problems for which many different models can be proposed. In such situation the ensemble approach is natural way to improve the final prediction results. In particular, we present th...
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Our structured prediction problem is formulated as a convex optimization problem of maximal margin [5-6], quite similar to the formulation of multiclass support vector machines (MSVM) [8]. It is applied to predict cos...
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the problem of finding a mediator to compose secured services has been reduced in our former work to the problem of solving deducibility constraints similar to those employed for cryptographic protocol analysis. We ex...
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In the present paper, a new extension to Generalized Nets is proposed. Tokens are represented as linked data and business process activities are represented as GN transitions. We will demonstrate how a GN model can be...
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ISBN:
(纸本)9781467322768
In the present paper, a new extension to Generalized Nets is proposed. Tokens are represented as linked data and business process activities are represented as GN transitions. We will demonstrate how a GN model can be used for describing a synchronized process with a rich data model and explain the respective advantages.
the problem of diagnosing Pima Indian Diabetes from data obtained from the UCI Repository of Machine Learning Databases[6] is handled with a modified Support Vector Machine strategy. Performance comparison with previo...
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the problem of diagnosing Pima Indian Diabetes from data obtained from the UCI Repository of Machine Learning Databases[6] is handled with a modified Support Vector Machine strategy. Performance comparison with previous studies is presented in order to demonstrate the proposed algorithm's advantages over various classification methods. the goal of the paper is to provide the grasp of a methodology that can be efficiently used to raise classification success rates obtained by the use of conventional approaches such as Neural Networks, RBF networks and K-nearest neighbors. the suggested algorithm divides the training set into two subsets: one that arises from the joining of coherent data regions and one that comprises of the data portion that is difficult to be clustered. Consequently, the first subset is used to train a Support Vector Machine with a RBF kernel and the second subset is used to train another Support Vector Machine with a polynomial kernel. During classification the algorithm is capable of identifying which of the two Support Vector Machine models to use. the intuition behind the suggested algorithm relies on the expectation that the RBF Support Vector Machine model is more appropriate to use on data sets of different characteristics than the polynomial kernel. In the specific study case the suggested algorithm raised average classification success rate to 82.2% while the best performance obtained by previous studies was 81% given by a fine tuned highly complex ARTMAP-IC model.
We present a new method for drug bioactivity classification based on learning an ensemble of multi-task classifiers. As the base classifiers of the ensemble we use Max-Margin Conditional Random Field (MMCRF) models, w...
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ISBN:
(纸本)9783642248542
We present a new method for drug bioactivity classification based on learning an ensemble of multi-task classifiers. As the base classifiers of the ensemble we use Max-Margin Conditional Random Field (MMCRF) models, which have previously obtained the state-of-the-art accuracy in this problem. MMCRF relies on a graph structure coupling the set of tasks together, and thus turns the multi-task learning problem into a graph labeling problem. In our ensemble method the graphs of the base classifiers are random, constructed by random pairing or random spanning tree extraction over the set of tasks. We compare the ensemble approaches on datasets containing the cancer inhibition potential of drug-like molecules against 60 cancer cell lines. In our experiments we find that ensembles based on random graphs surpass the accuracy of single SVM as well as a single MMCRF model relying on a graph built from auxiliary data.
In evolutionary graph theory [1] biologists study the problem of determining the probability that a small number of mutants overtake a population that is structured on a weighted, possibly directed graph. Currently Mo...
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ISBN:
(纸本)9780889869042
In evolutionary graph theory [1] biologists study the problem of determining the probability that a small number of mutants overtake a population that is structured on a weighted, possibly directed graph. Currently Monte Carlo simulations are used for estimating such fixation probabilities on directed graphs, since no good analytical methods exist. In this paper, we introduce a novel deterministic algorithm for computing fixation probabilities for strongly connected directed, weighted evolutionary graphs under the case of neutral drift, which we show to be a lower bound for the case where the mutant is more fit than the rest of the population (previously, this was only observed from simulation). We also show that, in neutral drift, fixation probability is additive under the weighted, directed case. We implement our algorithm and show experimentally that it consistently outperforms Monte Carlo simulations by several orders of magnitude, which can allow researchers to study fixation probability on much larger graphs.
the proceedings contain 34 papers. the topics discussed include: extending prior research with design science research: two patterns for DSRIS project generation;inductive design of maturity models: applying the rasch...
ISBN:
(纸本)9783642206320
the proceedings contain 34 papers. the topics discussed include: extending prior research with design science research: two patterns for DSRIS project generation;inductive design of maturity models: applying the rasch algorithm for design science research;pattern-based approach for designing with diagrammatic and propositional conceptual models;enacted software development routines based on waterfall and agile software methods: socio-technical event sequence study;a fitness-utility model for design science research;interface design elements for anti-phishing systems;experimental evaluation of peer endorsement system artifacts using best-of-breed ideals - effects of online decision confidence on post-choice regret;managing the future-six guidelines for designing environmental scanning systems;and utilizing user-group characteristics to improve acceptance of management support SystemState of the art and six design guidelines.
the proceedings contain 246 papers. the topics discussed include: visual mediation mechanisms for collaborative design and development;design for the information society;classifying interaction methods to support intu...
ISBN:
(纸本)9783642216718
the proceedings contain 246 papers. the topics discussed include: visual mediation mechanisms for collaborative design and development;design for the information society;classifying interaction methods to support intuitive interaction devices for creating user-centered-systems;evaluation of video game interfaces;standardizing user models;integral model of the area of reaches and forces of a disabled person with dysfunction of lower limbs as a tool in virtual assessment of manipulation possibilities in selected work environments;modeling the role of empathic design engaged personas: an emotional design approach;results of the technical validation of an accessible contact manager for mobile devices;developing accessible mobile phone applications: the case of a contact manager and real time text applications;a multitasking approach to adaptive spoken dialogue management;and from clouds to rain: consolidating and simplifying online communication services with easy one communicator.
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