Some human-centering issues for the Networld based on trust are discussed. People sometimes need help to understand how and when to shift from unjustified trust closer to justified distrust. Trust has aspects of an at...
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Some human-centering issues for the Networld based on trust are discussed. People sometimes need help to understand how and when to shift from unjustified trust closer to justified distrust. Trust has aspects of an attitude, an attribution, an expectation, a feeling or belief, an intention, and a trait. Trust can be thought of as a family of relations, certainly not a single relation. Interpersonal trust is both fundamentally and subtly different from trust in automation. Threshold effects are reflected in the tendency to maintain a fixed level of reliance even as the level of trust changes, resulting in a dichotomous pattern of reliance. Cybersecurity and defense issues, including social deception, misdirection, influence, and manipulation, span all the venues of macrocognitive work in the Networld. In macrocognitive work systems, trust can also be thought of as the expectation of reciprocity from others.
Support Vector machine (SVM) is sensitive to noises and outliers. For reducing the effect of noises and outliers, we propose a novel SVM for suppressing error function. The error function is limited to the interval of...
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ISBN:
(纸本)9781424449095
Support Vector machine (SVM) is sensitive to noises and outliers. For reducing the effect of noises and outliers, we propose a novel SVM for suppressing error function. The error function is limited to the interval of. The separation hypersurface is simplified and the margin of hypersurface is widened. Experimental results show that our proposed method is able to simultaneously increase the classification efficiency and the generalization ability of the SVM.
During recent years, there are more and more high-quality information in the Web database. Thus, it is becoming more and more important to find the most relevant Web database to user's query. In this paper, we pro...
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Visual exploratory data analysis represents a well-accepted imaging modality for high-dimensional DCE-MRI-derived breast cancer data. We employ this paradigm for discriminating between malignant and benign lesions bas...
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Visual exploratory data analysis represents a well-accepted imaging modality for high-dimensional DCE-MRI-derived breast cancer data. We employ this paradigm for discriminating between malignant and benign lesions based on different shape descriptors thanks to proven and novel dimension reduction algorithms. We demonstrate that shape structure changes such as weighted 3D Krawtchouck moments outperform global averaging moments such as geometric moment invariants in terms of discrimination of benign/malignant lesions. The best visualization of tumor shapes in a two-dimensional space is achieved based on nonlinear mapping methods, especially the ones that consider neighborhood ranks.
Text categorization (TC) is an important component in many information organization and information management tasks. Two key issues in TC are feature coding and classifier design. The Euclidean distance is usually ch...
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Text categorization (TC) is an important component in many information organization and information management tasks. Two key issues in TC are feature coding and classifier design. The Euclidean distance is usually chosen as the similarity measure in K-nearest neighbor classification algorithm. All the features of each vector have different functions in describing samples. So we can decide different function of every feature by using feature weight learning. In this paper text categorization via K-nearest neighbor algorithm based on feature weight learning is described. The numerical experiments prove the validity of this learning algorithm.
Concept discovery and modeling are fundamental problems in machinelearning research. Real world concepts are usually high-dimensional and have complicated distributions along their dimensions. Gaussian Mixture Models...
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ISBN:
(纸本)9789746152969
Concept discovery and modeling are fundamental problems in machinelearning research. Real world concepts are usually high-dimensional and have complicated distributions along their dimensions. Gaussian Mixture Models(GMM) have proved useful in modeling such complicated distributions. We propose a data-driven concept modeling and discovery framework using GMM, with on-line updating mechanism for fast computation suitable for real world applications. Experiments show the efficacy and efficiency of the proposed algorithm.
In this study, we study set operations on type-2 fuzzy sets. We first discuss join and meet operations of membership grades of type-2 fuzzy sets under left continuous t-norms and derive distributive law of type-2 fuzz...
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In this study, we study set operations on type-2 fuzzy sets. We first discuss join and meet operations of membership grades of type-2 fuzzy sets under left continuous t-norms and derive distributive law of type-2 fuzzy sets. Then, some properties on compositions of fuzzy relations is discussed. We derived that the distributive laws under union and composition of type-2 fuzzy relations is valid. An example shows the failure of distributive laws under intersection and composition.
A new method to solve the convex hull problem in n-dimensional spaces is proposed in this paper. At each step, a new point is added into the convex hull if the point is judged to be out of the current convex hull by a...
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A new method to solve the convex hull problem in n-dimensional spaces is proposed in this paper. At each step, a new point is added into the convex hull if the point is judged to be out of the current convex hull by a linear programming model. For the linear separable classification problem, if an instance is regarded as a point of the instances space, the overlap does not still occur between the convex hulls of different classes after a feature is deleted, then we can delete that feature. Repeat this process, an algorithm for feature selection is given. Experimental results show the effectiveness of the algorithm.
MCS (Minimal Consistent Set) is one of the classical algorithms for minimal consistent subset selection problem. However, when noisy samples are present classification accuracy can suffer. In addition, noise affect th...
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MCS (Minimal Consistent Set) is one of the classical algorithms for minimal consistent subset selection problem. However, when noisy samples are present classification accuracy can suffer. In addition, noise affect the size of minimal consistent set. Therefore, removing noise is an important issue before sample selection. In this paper, an improvement approach based on MCS to select the representative samples is proposed. Compared with other algorithms which remove the noise by Wilson Editing in advance for the representative samples selection, this algorithm performs the processes of noise removing and samples selection simultaneously. According to this method, most noise can be deleted and the most representative samples can be identified and retained. The experiments show that the proposed method can greatly remove the redundant samples and noise as well as increase the accuracy of solutions when it is used for classification tasks.
The feature extraction is the most key technologyof text *** word is used as the feature in the traditional text classification,and its effect forthe text classification is *** featureextraction method using base phra...
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The feature extraction is the most key technologyof text *** word is used as the feature in the traditional text classification,and its effect forthe text classification is *** featureextraction method using base phrase and keywordchanges the feature extraction of Chinese text fromsyntax and semantic *** the first,analyzing thefeature of baseNP and basedVP,and then make somewords into baseNP and baseVP which accord to therules of phrase,give WSD to other words in the *** paper proposes a stepwise feature extraction fromword to *** experiment results show that thismethod can perform much better than traditionalfeature extraction method it can improve the textclassification precision and recall.
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