divergence measure
A non symmetric divergence and kullback leibler divergence measure
... Information divergence measures and their bounds are well known in the literature of Information -symmetric information divergence symmetric divergence measure in terms of Kullback- Leibler ...
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A divergence measure for survival distributions
... new divergence measure between two survival distributions where the main criteria for the measure were that it had to be practical, have medical motivations, be symmetric, non-negative and be dynamic ...
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CLDC: Efficient Classification of Medical Data Using Class Level Disease Convergence Divergence Measure
... similarity measure being ...similarity measure plays the important role in ...similarity measure being used for both clustering and ...similarity measure for classification, the clustering ...
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Quantile Approach of Dynamic Generalized Entropy (Divergence) Measure
... and divergence measure of order (α, β) for residual and reversed residual (past) lifetime using the QFs and proved some characterization results of ...
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A General Divergence Measure for Monotonic Functions and Applications in Information Theory
... CSISZ ´ AR, Eine informationstheoretische Ungleichung und ihre Anwendung auf den Beweis der Ergodizit¨ at von Markoffschen Ketten.. (German) Magyar Tud.[r] ...
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Some New Inequalities for Jeffreys Divergence Measure in Information Theory
... Assume that a set χ and the σ − finite measure µ are given. Consider the set of all probability densities on µ to be Ω := n p | p : χ → R , p (x) ≥ 0, R χ p (x) dµ (x) = 1 o . The Kullback-Leibler ...
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A Generalization of f-Divergence Measure to Convex Functions Defined on Linear Spaces
... generalized measure of information, a “dis- tance function” on the set of probability distribution P n : The restriction here to discrete distributions is only for convenience, similar results hold for general ...
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Intuitionistic Fuzzy Jensen-Rényi Divergence: Applications to Multiple-Attribute Decision Making
... Jensen-Rényi divergence (IFJRD) between two intuitionistic fuzzy sets is ...proposed measure are ...Jensen-Rényi divergence measure to multiple-attribute decision making are illustrated and ...
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Fast Depth-Based Subgraph Kernels for Unattributed Graphs
... Jensen-Shannon divergence measure between a pair of centroid expansion subgraphs only requires computation of quadratic vertex number, even a pair of large global graphs being ...to measure more ...
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Performance evaluation of data aggregation for cluster-based wireless sensor network
... proposed Divergence Measure based Clustering (DMC) + Entropy based Data Aggregation (EDA) along with Hybrid Energy-Efficient Distributed Clustering (HEED) and Belief Propagation (BP) ...using ...
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New information inequalities on new f divergence by using ostrowski’s inequalities and its application
... i.e., divergence measures are directly propositional to the distance between two probability ...any divergence measure must take its minimum value zero when probability distributions are equal and ...
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A Divergence Measure for Combining Super-Efficiency Scores in Performance Measurement of Two-Stage Production Systems
... J-divergence measure are entropy-based mea- sures that quantify discrepancies between distri- butions; so it is useful for our comparison pur- ...precisely measure differ- ences between these ...
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IPQpq …….(1.1)
... The divergence measure is applied on numerous areas like probability measure, pattern recognition, signal processing, economics ...information, measure the amount of information about an ...
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On Intuitionistic Fuzzy Entropy as Cost Function in Image Denoising
... fuzzy divergence measure of the denoised image C ˜ and original image ...its divergence of original image is ...fuzzy divergence measure are minimum, the sum that is the cost value will ...
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First evidence for postzygotic reproductive isolation between two populations of Eurasian perch (Perca fluviatilis L.) within Lake Constance
... Rice and Hostert [22] showed that hybrids of two ecotypes can suffer reduced fitness, both because they may fall between distinct ecological niches of their parental popu- lations and be selected against (extrinsic ...
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Information Theory Interpretation of the Skip Gram Negative Sampling Objective Function
... S(m) is the information loss caused by the low- dimensional embedding. We can view it as a Jensen-Shannon variant of the information bottle- neck principle (Tishby et al., 1999; Globerson et al., 2007) that is defined in ...
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Detecting Cross Lingual Semantic Divergence for Neural Machine Translation
... to measure the impact of semantic divergence on translation quality when used as a data selection criterion: if our hypothe- sis holds, then training on non-divergent examples should yield better ...
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Approximations of Shannon Mutual Information for Discrete Variables with Applications to Neural Population Coding
... We have derived several asymptotic bounds and effective approximations of mutual information for discrete variables and established several relationships among different approximations. Our final approximation formulas ...
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Research Hotspots Evolving Action Detection based on Time Sequence Journal Topic Model
... KL divergence to measure similarity, making a detection of continuing, emerging, splitting, amalgamating or disappearing between two neighbor topic-snapshots which realize the particle analysis of journal ...
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An Empirical Investigation of Discounting in Cross Domain Language Models
... This result is surprising in light of the constant discounts observed for the NYT95/NYT95 0 pair. Goodman (2001) proposes that discounts arise from document-level “burstiness” in a corpus, because language often repeats ...
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