27379775
Convergence of a New Decomposition Algorithm for Support Vector Machines
Decomposition methods is the main way for solving support vector machines (SVMs) with large data sets. In this paper a new decomposition algorithm is proposed, and its convergence is also proved.
36699108
New Results for Global Exponential Stability of Delayed Cohen-Grossberg Neural Networks
The exponential stability is discussed for Cohen-Grossberg neural networks with discrete delays. Without assuming the boundedness, differentiability and monotonicity of the activation functions, the nonlinear measure approach is employed to analyze the existence and uniqueness of an equilibrium, and a novel Lyapunov functional is constructed to investigate the exponential stability of the networks. New general sufficient conditions, which are independent of the delays, are derived for the global exponential stability of the delayed neural networks.
10388908
A reference model approach to stability analysis of neural networks
In this paper, a novel methodology called a reference model approach to stability analysis of neural networks is proposed. The core of the new approach is to study a neural network model with reference to other related models, so that different modeling approaches can be combinatively used and powerfully cross-fertilized. Focused on two representative neural network modeling approaches (the neuron state modeling approach and the local field modeling approach), we establish a rigorous theoretical basis on the feasibility and efficiency of the reference model approach. The new approach has been used to develop a series of new, generic stability theories for various neural network models. These results have been applied to several typical neural network systems including the Hopfield-type neural networks, the recurrent back-propagation neural networks, the BSB-type neural networks, the bound-constraints optimization neural networks, and the cellular neural networks. The results obtained unify, sharpen or generalize most of the existing stability assertions, and illustrate the feasibility and power of the new method.
10975724
Adding Semantics to Email Clustering
This paper presents a novel algorithm to cluster emails according to their contents and the sentence styles of their subject lines. In our algorithm, natural language processing techniques and frequent itemset mining techniques are utilized to automatically generate meaningful generalized sentence patterns (GSPs) from subjects of emails. Then we put forward a novel unsupervised approach which treats GSPs as pseudo class labels and conduct email clustering in a supervised manner, although no human labeling is involved. Our proposed algorithm is not only expected to improve the clustering performance, it can also provide meaningful descriptions of the resulted clusters by the GSPs. Experimental results on open dataset (Enron email dataset) and a personal email dataset collected by ourselves demonstrate that the proposed algorithm outperforms the K-means algorithm in terms of the popular measurement Fl. Furthermore, the cluster naming readability is improved by 68.5% on the personal email dataset.
44569612
A New Cache Replacement Algorithm in SMO
In the methods for training Support Vector Machines (SVM), precomputed elements in the Hessian matrix are usually cached in order to avoid recomputation. However, the least-recent-used replacement algorithm that is widely used is not suitable since the elements are requested in an irregular way. A new cache replacement algorithm applied in Sequential Minimal Optimization (SMO) is presented in the paper. The item corresponding to the component with minimal violation of the Karush-Kuhn-Tucher (KKT) condition is deleted to make room for new one when the cache is full. It is shown in the experiments that the hit ratio of the cache is improved compared with LRU cache while the training time can be reduced in the tasks where the computation of elements in Hessian matrix is very time-consuming.
36606756
Optimization of combustion control based on fuzzy logic
In this paper, to reach energy-saving goal, fuzzy system is employed to get optimal setpoints of furnace temperature. In the proposed approach, the optimal temperature trajectory of a slab in the furnace is determined through fuzzy system and the setpoint of furnace temperature is calculated according to this optimal trajectory. Because the approach calculates the setpoints of furnace temperature at each computation period, it can deal with disturbance of system to a certain extent. The simulation results show validity of the proposed method.
62394
Support vector machine learning for image retrieval
A novel method of relevance feedback is presented based on support vector machine learning in the content-based image retrieval system. A SVM classifier can be learned from training data of relevance images and irrelevance images marked by users. Using the classifier, the system can retrieve more images relevant to the query in the database efficiently. Experiments were carried out on a large-size database of 9918 images. It shows that the interactive learning and retrieval process can find correct images increasingly. It also shows the generalization ability of SVM under the condition of limited training samples.
34054461
Fuzzy system with adaptive rulebase
A fuzzy model is constructed out of recorded data in which an adaptive rulebase is introduced into fuzzy system modeling. Usually, the rulebase of a fuzzy system is generated and optimized offline once and for all. However, due to uncertainty and change of state of the system, this rulebase may not work well when the model is put into use. Hence, a certain length of recorded data from the current time instance is employed to extract rules and the rulebase is renewed by these rules. In the proposed method, the structure of the fuzzy model is determined in advance, then recorded data are used to extract fuzzy rules and construct a fuzzy model. In the application of the model, the rulebase is renewed in every computation period and this is a rolling process. This approach can deal with a system with much uncertainty since these newly generated rules reflect the most current state of the system. To demonstrate the effectiveness of the proposed method, it is used to build a model of a furnace and simulation results are satisfactory.