Most works on Support Vector Regression (SVR) focus on kernel or loss functions, with the corresponding support vectors obtained using a fixed-radius e -tube, affording good predictive performance on datasets. However...
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Most works on Support Vector Regression (SVR) focus on kernel or loss functions, with the corresponding support vectors obtained using a fixed-radius e -tube, affording good predictive performance on datasets. However, the fixed radius limitation prevents the adaptive selection of support vectors according to the data distribution characteristics, compromising the performance of the SVR-based methods. Therefore, this study proposes an "Alterable e( i) -Support Vector Regression" ( A e( i) -SVR) model by applying a novel e , named "Alterable e (i) ," to the SVR model. Based on the data point sparsity at each location, the model solves the different e (i )at the corresponding position, and thus zoom-in or zoom-out the e -tube by changing its radius. Such a variable e -tube strategy diminishes noise and outliers in the dataset, enhancing the prediction performance of the A e (i) -SVR model. Therefore, we suggest a novel non-deterministic algorithm to iteratively solve the complex problem of optimizing e (i) associated with every location. Extensive experimental results demonstrate that our approach can improve the accuracy and stability on simulated and real data compared with the baseline methods.
An analysis of the undecidability of Diophantine equations showed that problems of recognition of the properties of the NP class are decidable, i.e., a non-deterministic algorithm or exhaustive search at the problem i...
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An analysis of the undecidability of Diophantine equations showed that problems of recognition of the properties of the NP class are decidable, i.e., a non-deterministic algorithm or exhaustive search at the problem input gives a positive or negative answer. For polynomial Diophantine equations, such a non-deterministic algorithm does not exist. A simple version of Godel's theorem on the incompleteness of arithmetic follows from the undecidability of Diophantine equations.
Biometric-based verification system has emerged as a powerful authentication tool. Despite its advantages over traditional systems, it is prone to several attacks. These attacks may creep through the biometric system ...
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Biometric-based verification system has emerged as a powerful authentication tool. Despite its advantages over traditional systems, it is prone to several attacks. These attacks may creep through the biometric system and may prove fatal if it is not robust enough. One such attack, known as replay attack, relates to replaying of illegally intercepted data has been least explored with respect to biometrics. The paper proposes a non-deterministic approach to iris recognition and attempts to show its utility in allaying replay attack over iris recognition system. The system determines robust iris regions for each eye using LBP-based feature extraction and involves the use of randomly selected subsets of these regions for authentication. These data, even if intercepted, are useless as the non-deterministic nature of technique will require a differently ordered subset of regions for each authentication. The performance of this system and its effectiveness in allaying replay attack has been shown experimentally. The results have been compared with existing state-of-art techniques with respect to iris recognition and replay attack. The impact of hill climbing attack on the proposed approach has also been discussed as it has been proved, by various researchers, to be critical to the performance of a biometric system.
In this paper we study heuristic proof systems and heuristic non-deterministic algorithms. We give an example of a language Y and a polynomial-time samplable distribution D such that the distributional problem (Y, D) ...
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In this paper we study heuristic proof systems and heuristic non-deterministic algorithms. We give an example of a language Y and a polynomial-time samplable distribution D such that the distributional problem (Y, D) belongs to the complexity class HeurNP but Y is not an element of NP if NP not equal coNP, and (Y, D) is not an element of HeurBPP if (NP, PSamp) not subset of HeurBPP. For a language L and a polynomial q we define the language pad(q)(L) composed of pairs (x, r) where x is an element of L and r is an arbitrary binary string of length at least q(vertical bar x vertical bar). If D = {D-n}(n=1)(infinity) is an ensemble of distributions on strings, let D x U-q be a distribution on pairs (x, r), where x is distributed according to D-n and r is uniformly distributed on strings of length q(n). We show that for every language L in A M there is a polynomial q such that for every distribution D concentrated on the complement of L the distributional problem (pad(q)(L), D x U-q) has a polynomially bounded heuristic proof system. Since graph non-isomorphism (GNI) is in A M, the above result is applicable to GNI.
DNA self-assembly technology has brought novel inspirations to the development of DNA computing Diversified computational models based on DNA self-assembly have been used to solve various NP problems. In this paper, a...
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DNA self-assembly technology has brought novel inspirations to the development of DNA computing Diversified computational models based on DNA self-assembly have been used to solve various NP problems. In this paper, a 3D DNA self-assembly model is presented to solve the Graph Vertex Coloring problem. With the capacity of DNA molecules in massive parallel computation, the model can simulate a non-deterministic algorithm and solve the problem in linear time Theta(n) The number of distinct tiles used in the model is Theta(k(2)), where k is the size of the color set For the vertex 3-coloring problem, the model requires only 22 types of distinct tiles. Our work makes a significant attempt for exploring the computational power of 3D DNA self-assembly
The Turing machine model is extended to allow for recursive calls and the basic theory of these machines is developed. The model is also used to study the following additional topics: The time and storage needed to im...
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The Turing machine model is extended to allow for recursive calls and the basic theory of these machines is developed. The model is also used to study the following additional topics: The time and storage needed to implement recursive algorithms by non-recursive algorithms, the storage needed to implement non-deterministic algorithms by deterministicalgorithms, and the implementation of recursive algorithms by means of stack machines. Some attention is given to time bounds but the emphasis is on storage-bounded computations.
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