instruction:
In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
question:
Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. It is used to identify the co-expressed genes in specific cells or tissues that are actively used to make proteins. This method is used to analysis the gene expression, an important task in bioinformatics research. Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genes, biologically relevant groupings of genes and samples. In this paper we applied K-Means with Automatic Generations of Merge Factor for ISODATAAGMFI. Though AGMFI has been applied for clustering of Gene Expression Data, this proposed Enhanced Automatic Generations of Merge Factor for ISODATAEAGMFI Algorithms overcome the drawbacks of AGMFI in terms of specifying the optimal number of clusters and initialization of good cluster centroids. Experimental results on Gene Expression Data show that the proposed EAGMFI algorithms could identify compact clusters with perform well in terms of the Silhouette Coefficients cluster measure.
answer:
Performance Analysis of Enhanced Clustering Algorithm for Gene Expression Data


question:
Evolution of visual object recognition architectures based on Convolutional Neural Networks & Convolutional Deep Belief Networks paradigms has revolutionized artificial Vision Science. These architectures extract & learn the real world hierarchical visual features utilizing supervised & unsupervised learning approaches respectively. Both the approaches yet cannot scale up realistically to provide recognition for a very large number of objects as high as 10K. We propose a two level hierarchical deep learning architecture inspired by divide & conquer principle that decomposes the large scale recognition architecture into root & leaf level model architectures. Each of the root & leaf level models is trained exclusively to provide superior results than possible by any 1-level deep learning architecture prevalent today. The proposed architecture classifies objects in two steps. In the first step the root level model classifies the object in a high level category. In the second step, the leaf level recognition model for the recognized high level category is selected among all the leaf models. This leaf level model is presented with the same input object image which classifies it in a specific category. Also we propose a blend of leaf level models trained with either supervised or unsupervised learning approaches. Unsupervised learning is suitable whenever labelled data is scarce for the specific leaf level models. Currently the training of leaf level models is in progress; where we have trained 25 out of the total 47 leaf level models as of now. We have trained the leaf models with the best case top-5 error rate of 3.2% on the validation data set for the particular leaf models. Also we demonstrate that the validation error of the leaf level models saturates towards the above mentioned accuracy as the number of epochs are increased to more than sixty. The top-5 error rate for the entire two-level architecture needs to be computed in conjunction with the error rates of root & all the leaf models. The realization of this two level visual recognition architecture will greatly enhance the accuracy of the large scale object recognition scenarios demanded by the use cases as diverse as drone vision, augmented reality, retail, image search & retrieval, robotic navigation, targeted advertisements etc.
answer:
HIERARCHICAL DEEP LEARNING ARCHITECTURE FOR 10K OBJECTS CLASSIFICATION


question:
<lb>We study the problem of adaptive control of a high dimensional linear quadratic<lb>(LQ) system. Previous work established the asymptotic convergence to an optimal<lb>controller for various adaptive control schemes. More recently, for the average<lb>cost LQ problem, a regret bound of O(<lb>√<lb>T ) was shown, apart form logarithmic<lb>factors. However, this bound scales exponentially with p, the dimension of the<lb>state space. In this work we consider the case where the matrices describing the<lb>dynamic of the LQ system are sparse and their dimensions are large. We present<lb>an adaptive control scheme that achieves a regret bound of O(p<lb>√<lb>T ), apart from<lb>logarithmic factors. In particular, our algorithm has an average cost of (1 + ǫ)<lb>times the optimum cost after T = polylog(p)O(1/ǫ). This is in comparison to<lb>previous work on the dense dynamics where the algorithm requires time that scales<lb>exponentially with dimension in order to achieve regret of ǫ times the optimal cost.<lb>We believe that our result has prominent applications in the emerging area of<lb>computational advertising, in particular targeted online advertising and advertising<lb>in social networks.
answer:

Efficient Reinforcement Learning for High Dimensional Linear Quadratic Systems