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In this paper, we propose a robust proximal classifier via absolute value inequalities (AVIPC) for pattern classification. AVIPC determines K proximal planes by solving K optimization problems with absolute value inequalities. In AVIPC, each proximal plane is closer to one class and far away from the others. By using the absolute value inequalities, AVIPC is more robust and sparse than traditional...
Graph classification has traditionally focused on graphs generated from a single feature view. In many applications, it is common to have useful information from different channels/views to describe objects, which naturally results in a new representation with multiple graphs generated from different feature views being used to describe one object. In this paper, we formulate a new Multi-Graph-View...
Face recognition plays a fairly vital role in human computer inter-action. Eigenface algorithm is one representative approach that projects face images onto a low dimensional feature space using principal components analysis (PCA) to choose the maximal total scatter across all classes. However, due to the linear combination of original samples, eigenfaces may be difficult to interpret and explain...
This paper describes an acquisition system which has been used to demonstrate the usefulness of problem-based pedagogy. This system was based on a high-performance microcontroller, an integrated temperature and humidity sensor and real-time data hard drive storage functionality. According to the characteristic analysis method, this paper illustrated the benefits of sensor network optimization by applying...
Hyperspectral image classification is one of the most significant topics in remote sensing. A large number of methods have been proposed to improve the classification accuracy. However, the improvement often comes at the cost of higher complexity. In this work, we mainly focus on the Markov Random Fields related paradigm, which involves a demanding energy minimization procedure. Traditional methods...
For the problems that college engineering training teaching management system's page style is clutter and it is difficult for the later maintenance. Introduce a scheme that uses the latest Evolutility4.0 to optimize the college engineering training teaching management system on the basis of the original. To make the system's page simple and unified, easy and simple to handle. While reducing later...
This paper deals how genetic programming can be used for record de-duplication. Many systems rely on the data integrity for offering high quality services that may be affected by the existence of near-replicas, quasi-replicas, or replicas entries in their repositories. So, there has been a huge effort from private and public organizations for developing effective methods for removing duplicates from...
Multitask Learning has been proven to be more effective than the traditional single task learning on many real-world problems by simultaneously transferring knowledge among different tasks which may suffer from limited labeled data. However, in order to build a reliable multitask learning model, nontrivial effort to construct the relatedness between different tasks is critical. When the number of...
We propose a gossip-based mini-batch random projection (GMRP) algorithm that can reduce communication overhead for a distributed optimization problem defined over a network with a very large number of constraints. We state a convergence result and provide an application of the GMRP, text classification with support vector machines.
We introduce an approach to sparsity penalized multi-class classifier design that accounts for multi-block structure of the data. The unified multi-class classifier is parameterized by a set of weights defined over the classes and over the blocks. The proposed sparse multi-block multi-class classifier imposes structured sparsity on the weights so that the same variables are selected for all classes...
Natural language dialogue is an important component of interaction between ordinary users and complex computer applications. Short Text Semantic Similarity algorithms have been developed to improve the efficiency of producing sophisticated dialogue systems. Such algorithms are currently unable to discriminate between different dialogue acts (assertions, questions, instructions etc.), requiring the...
This paper analyzes the classification of hyperspectral images with the sparse representation algorithm in the presence of a minimal reconstruction error. Incorporating the contextual information into the sparse recovery process can improve the classification performance. However, previous sparse algorithms using contextual information only assume that all neighbors around a test sample make equal...
Increase in the amount of information on the Web has caused the need for accurate automated classifiers for Web pages to maintain Web directories and to increase search engines' performance. As every (HTML/XML) tag and every term on each Web page can be considered as a feature, we need efficient methods to select best features to reduce feature space of the Web page classification problem. In this...
Based on the analysis of The BP Neural Network's structrue and drawbacks, the article uses the Genetic Algorithms to optimize initial weights and thresholds. It still uses function simulation to compare these two neural networks based on the MATLAB. The results show that GA-BP neural networks can reduce the function time and make it more scientific.
Protein Structure Prediction (PSP) has significant applications in the fields of drug design, disease prediction and so on. Since PSP has been a great confrontation in the field of Protein Folding Research, this paper presents a novel method for protein using Structural Concealed Markov Model (SCMM). Typically, the contribution of this work has been made for appropriate mapping of protein primary...
Support vector machine (SVM) is a popular method for classification in data mining. The canonical duality theory provides a unified analytic solution to a wide range of discrete and continuous problems in global optimization. This paper presents a canonical duality approach for solving support vector machine problem. It is shown that by the canonical duality, these nonconvex and integer optimization...
Real world data mining applications such as Mine Countermeasure Missions (MCM) involve learning from imbalanced data sets, which contain very few instances of the minority classes and many instances of the majority class. For instance, the number of naturally occurring clutter objects (such as rocks) that are detected typically far outweighs the relatively rare event of detecting a mine. In this paper...
In order to overcome the problem that it is difficult for support vector machine to deal with uncertain information system, fuzzy theory and rough set are introduced to get two uncertain support vector machines, which are fuzzy support vector machine and fuzzy rough support vector machine respectively. And the principle of these two uncertain methods reducing the effect of uncertain information is...
The establishment of the business industrial park provides a favorable environment to small and medium-sized electronic commerce enterprises. It helps the electronic commerce enterprise to hatch, and the e-commerce industrial park is the entity reflecting of the electronic commerce industrial cluster. Through the functional orientation of the electronic commerce industrial park and the analysis of...
In today's aircrew training context, although there is an abundance of training systems that can enhance training and reduce costs, the challenge for the military training organizations to select the most cost-effective training systems to address their immediate and future needs is unresolved. The urgency of this dilemma is exacerbated by shrinking defense budgets. This paper shows how the systems...
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