Derivative free optimization methods have recently gained a lot of attractions for neural learning. the curse of dimensionality for the neural learning problem makes local optimization methods very attractive;however ...
详细信息
ISBN:
(纸本)3540269231
Derivative free optimization methods have recently gained a lot of attractions for neural learning. the curse of dimensionality for the neural learning problem makes local optimization methods very attractive;however the error surface contains many local minima. Discrete gradient method is a special case of derivative free methods based on bundle methods and has the ability to jump over many local minima. there are two types of problems that are associated withthis when local optimization methods are used for neural learning. the first type of problems is initial sensitivity dependence problem - that is commonly solved by using a hybrid model. Our early research has shown that discrete gradient method combining with other global methods such as evolutionary algorithm makes them even more attractive. these types of hybrid models have been studied by other researchers also. Another less mentioned problem is the problem of large weight values for the synaptic connections of the network. Large synaptic weight values often lead to the problem of paralysis and convergence problem especially when a hybrid model is used for fine tuning the learning task. In this paper we study and analyse the effect of different regularization parameters for our objective function to restrict the weight values without compromising the classification accuracy.
Classification is one of the main tasks in machinelearning, datamining and patternrecognition. Compared withthe extensively studied data-driven approaches, the interactively user-driven approaches are less explore...
详细信息
ISBN:
(纸本)0780391365
Classification is one of the main tasks in machinelearning, datamining and patternrecognition. Compared withthe extensively studied data-driven approaches, the interactively user-driven approaches are less explored A granular computing model is suggested for re-examiningthe classification problems. An interactive classification method using the granule network is proposed, which allows multi-strategies for granule tree construction and enhances the understanding and interpretation of the classification process. this method is complementary to the existing classification methods.
Real life transaction data often miss some occurrences of items that are actually present. As a consequence some potentially interesting frequent patterns cannot be discovered, since with exact matching the number of ...
详细信息
this paper discusses a consistency in patterns of language use across domain-specific collections of text. We present a method for the automatic identification of domain-specific keywords - specialist terms - based on...
详细信息
the proceedings contain 60 papers. the topics discussed include: predicting software suitability using a Bayesian belief network;parallel algorithm for control chart patternrecognition;data-centric automated data min...
详细信息
ISBN:
(纸本)0769524958
the proceedings contain 60 papers. the topics discussed include: predicting software suitability using a Bayesian belief network;parallel algorithm for control chart patternrecognition;data-centric automated datamining;a Bayesian kernel for the prediction of neuron properties from binary gene profiles;new filter-based feature selection criteria for identifying differentially expressed genes;a new clustering algorithm using message passing and its applications in analyzing microarray data;iterative weighting of phylogenetic profiles increases classification accuracy;integrating knowledge-driven and data-driven approaches for the derivation of clinical prediction rules;sparse classifiers for automated heart wall motion abnormality detection;segmenting brain tumors using alignment-based features;and the application of machinelearning techniques to the prediction of erectile dysfunction.
Genomic data clustering has been receiving a growing attention during last years. However, finding the biological meaning of the clusters is still a manual work, which becomes very difficult as the amount of data grow...
详细信息
According to the sizes of the attribute set and the information table, the information tables are categorized into three types of Rough Set problems, patternrecognition/machinelearning problems, and Statistical Mode...
详细信息
ISBN:
(纸本)3540286535
According to the sizes of the attribute set and the information table, the information tables are categorized into three types of Rough Set problems, patternrecognition/machinelearning problems, and Statistical Model Identification problems. In the first Rough Set situation, what we have seen is as follows: 1) the "granularity" should be taken so as to divide equally the unseen tuples out of the information table, 2) the traditional "Reduction" sense accords withthe above insistence, and 3) the "stable" subsets of tuples, which are defined through a "Galois connection" between the subset and the corresponding attribute subset, may play an important role to capture some characteristics that can be read from the given information table. We show these with some illustrative examples.
We estimate the speed of texture change by measuring the spread of texture vectors in their feature space. this method allows us to robustly detect even very slow moving objects. By learning a normal amount of texture...
详细信息
In this paper we address confidentiality issues in distributed data clustering, particularly the inference problem. We present a measure of inference risk as a function of reconstruction precision and number of collud...
详细信息
暂无评论