Committee machines approach has shown to be useful in different applications. Protein primary structure data contain valuable information to extract. In this paper we mine these data and predict protein contact map ba...
详细信息
ISBN:
(纸本)9781424422104
Committee machines approach has shown to be useful in different applications. Protein primary structure data contain valuable information to extract. In this paper we mine these data and predict protein contact map based on committee machines. Contact map is the simplified, two dimensional representation of protein spatial structure. Contact map prediction is of great interest due to its application in fold recognition and predicting protein tertiary structure. the results show that the performance of the committee is considerably better than a single model.
Analysis of contemporary information security systems (ISS) and especially the case of intrusion detection systems (IDS) shows us few character negative features and drawbacks. Original methods and combined anomaly an...
详细信息
ISBN:
(纸本)9781424417391
Analysis of contemporary information security systems (ISS) and especially the case of intrusion detection systems (IDS) shows us few character negative features and drawbacks. Original methods and combined anomaly and signature IDS applications are presented in the paper. Human-centered methods INCONSISTENCY, FUNNEL, CALEIDOSCOPE and CROSSWORD interact on a competitive principle and are controlled by a synthetic metamethod SMM. A research is going on for the possibilities of including other machinelearning and datamining methods under the general control of SMM. their applications aim at computational discovery and knowledge acquisition. It is reinforced by identification and resolution of contradictions, self-learning and other methods for analysis of different types of models from the ISS domain. the complexity of application results is considered the data analysis in the field frequently needs an act of creation especially if it is applied in a knowledge-poor environment. It is shown that even in this case the creative processes are based on applications of clear and well-formalized methods.
Neural networks have become increasingly important in areas such as medical diagnosis, bio-informatics, intrusion detection, and homeland security. In most of these applications, one major issue is preserving privacy ...
详细信息
ISBN:
(纸本)9781424417391
Neural networks have become increasingly important in areas such as medical diagnosis, bio-informatics, intrusion detection, and homeland security. In most of these applications, one major issue is preserving privacy of individual's private information and sensitive data. In this paper, we propose two secure protocols for perceptron learning algorithm when input data is horizontally and vertically partitioned among the parties. these protocols can be applied in both linearly separable and non-separable datasets, while not only data belonging to each party remains private, but the final learning model is also securely shared among those parties. Parties then can jointly and securely apply the constructed model to predict the output corresponding to their target data. Also, these protocols can be used incrementally, i.e. they process new coming data, adjusting the previously constructed network.
Manifold learning has currently become a hot issue in the field of machinelearning, patternrecognition and datamining. Locally linear embedding (LLE) is one of several promising manifold learning methods. But ordin...
详细信息
ISBN:
(纸本)9780769533056
Manifold learning has currently become a hot issue in the field of machinelearning, patternrecognition and datamining. Locally linear embedding (LLE) is one of several promising manifold learning methods. But ordinary LLE can not distinguish effectively the low-dimensional embeddings of noise data. By introducing the reconstruction similarity into LLE, this paper proposes a generalized locally linear embedding algorithm based on local reconstruction similarity. Experimental results show on Columbia object image datathat the new generalized version is superior to LLE in revealing the visualization of high-dimensional image dataset containing noise images.
this paper focuses on neural networks with complex-valued (CV) neurons as well as on selected aspects of neural networks learning, pruning and rule extraction. CV neurons can be used as versatile substitutes in real-v...
详细信息
ISBN:
(纸本)9781424417391
this paper focuses on neural networks with complex-valued (CV) neurons as well as on selected aspects of neural networks learning, pruning and rule extraction. CV neurons can be used as versatile substitutes in real-valued perceptron networks. learning of CV layers is discussed in context of traditional multilayer feedforward architecture. Such learning is derivative-free and it usually requires networks of reduced size. Selected examples and applications of CV-networks in bioinformatics and patternrecognition are discussed. the paper also covers specialized learning techniques for logic rule extraction. Such techniques include learning with pruning, and can be used in expert systems, and other applications that rely on models developed to fit measured data.
Feature selection is an important task in machinelearning, patternrecognition and datamining. this paper proposed a new feature selection method for classification, named SD, which is based on scatter matrix used i...
详细信息
ISBN:
(纸本)9781424420957
Feature selection is an important task in machinelearning, patternrecognition and datamining. this paper proposed a new feature selection method for classification, named SD, which is based on scatter matrix used in linear discriminant analysis. the main feature of SD is its simplicity and independency of learning algorithms. High-dimensional data samples are first projected into a lower dimensional subspace of the original feature space by means of a linear transformation matrix, which can be attained according to the scatter degree of each feature, and then the scatter degree is used to measure the importance of each feature. A comparison of SD and some popular feature selection methods (information gain and X-2-test) is conducted, and the results of experiment carried out on 19 data sets show the advantages of SD.
A model selection method based on tabu search is proposed to build support vector machines (binary decision functions) of reduced complexity and efficient generalization. the aim is to build a fast and efficient suppo...
详细信息
A model selection method based on tabu search is proposed to build support vector machines (binary decision functions) of reduced complexity and efficient generalization. the aim is to build a fast and efficient support vector machines classifier. A criterion is defined to evaluate the decision function quality which blends recognition rate and the complexity of a binary decision functions together. the selection of the simplification level by vector quantization, of a feature subset and of support vector machines hyperparameters are performed by tabu search method to optimize the defined decision function quality criterion in order to find a good sub-optimal model on tractable times.
the goal of statistical pattern feature extraction (SPFE) is 'low loss dimension reduction'. As the key link of patternrecognition, dimension reduction has become the research hot spot and difficulty in the f...
详细信息
ISBN:
(纸本)9783540859833
the goal of statistical pattern feature extraction (SPFE) is 'low loss dimension reduction'. As the key link of patternrecognition, dimension reduction has become the research hot spot and difficulty in the fields of patternrecognition, machinelearning, datamining and so on. pattern feature extraction is one of the most challenging research fields and has attracted the attention from many scholars. this paper summarily introduces the basic principle of SPFE, and discusses the latest progress of SPFE from the aspects such as classical statistical theories and their modifications, kernel-based methods, wavelet analysis and its modifications, algorithms integration and so on. At last we discuss the development trend of SPFE.
mining association rules plays an essential role in datamining tasks. Many algorithms have been proposed for mining Boolean association rules, but they cannot deal with quantitative and categorical data directly. Alt...
详细信息
ISBN:
(纸本)9781424421077
mining association rules plays an essential role in datamining tasks. Many algorithms have been proposed for mining Boolean association rules, but they cannot deal with quantitative and categorical data directly. Although we can transform quantitative attributes into intervals and applying Boolean algorithms to the intervals. But this approach is not effective and is difficult to scale tip for high-dimensional cases. An efficient algorithm, DBSMiner (Density Based Sub-space Miner), is proposed by using the notion of "density- connected" to cluster the high density sub-space of quantitative attributes and gravitation between grid / cluster to deal withthe low density cells which may be missed by the previous algorithms, DBSMiner not only can solve the problems of previous approaches, but also can scale up well for high-dimensional cases. Evaluations on DBSMiner have been performed using the car and the shuttle databases maintained at the UCI machinelearning Repository. the results indicate that DBSMiner is effective and can scale up quite linearly with an increasing number of attributes.
the proceedings contain 70 papers. the special focus in this conference is on Intelligent Computing. the topics include: Adaptive routing algorithm in wireless communication networks using evolutionary algorithm;solvi...
ISBN:
(纸本)9783540859291
the proceedings contain 70 papers. the special focus in this conference is on Intelligent Computing. the topics include: Adaptive routing algorithm in wireless communication networks using evolutionary algorithm;solving vehicle routing problem using ant colony and genetic algorithm;a research on the association of pavement surface damages using datamining;an integrated method for gml application schema match;application of classification methods for forecasting mid-term power load patterns;design of fuzzy entropy for non convex membership function;higher-accuracy for identifying frequent items over real-time packet streams;privacy preserving sequential patternmining in data stream;a general k-level uncapacitated facility location problem;fourier series chaotic neural networks;shape matching based on ant colony optimization;simulation study on fuzzy markov chains;a tentative approach to minimal reducts by combining several algorithms;comparative study with fuzzy entropy and similarity measure;new structures of intuitionistic fuzzy groups;an illumination independent face verification based on gabor wavelet and supported vector machine;hardware deblocking filter and impact;new data pre-processing on assessing of obstructive sleep apnea syndrome and recognition of plant leaves using support vector machine.
暂无评论