scholarly journals Extreme Learning Machine with sigmoid activation function on large data

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3523-3526
Keyword(s):  
Efficient Algorithm ◽  
Large Data ◽  
Activation Function ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Set ◽  
Learning Machine ◽  
Sigmoid Activation Function ◽  
State Of Art ◽  
Better Than

This paper describes an efficient algorithm for classification in large data set. While many algorithms exist for classification, they are not suitable for larger contents and different data sets. For working with large data sets various ELM algorithms are available in literature. However the existing algorithms using fixed activation function and it may lead deficiency in working with large data. In this paper, we proposed novel ELM comply with sigmoid activation function. The experimental evaluations demonstrate the our ELM-S algorithm is performing better than ELM,SVM and other state of art algorithms on large data sets.

Estimating Intersection Control Delay Using Large Data Sets of Travel Time from a Global Positioning System

2005 ◽  
Vol 1917 (1) ◽  
pp. 18-27
Author(s):  
Brian Hoeschen ◽  
Darcy Bullock ◽  
Mark Schlappi
Keyword(s):  
Travel Time ◽  
Traffic Engineering ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Set ◽  
Control Delay ◽  
Diverse Data ◽  
Intersection Control ◽  
Better Than

Historically, stopped delay was used to characterize the operation of intersection movements because it was relatively easy to measure. During the past decade, the traffic engineering community has moved away from using stopped delay and now uses control delay. That measurement is more precise but quite difficult to extract from large data sets if strict definitions are used to derive the data. This paper evaluates two procedures for estimating control delay. The first is based on a historical approximation that control delay is 30% larger than stopped delay. The second is new and based on segment delay. The procedures are applied to a diverse data set collected in Phoenix, Arizona, and compared with control delay calculated by using the formal definition. The new approximation was observed to be better than the historical stopped delay procedure; it provided an accurate prediction of control delay. Because it is an approximation, this methodology would be most appropriately applied to large data sets collected from travel time studies for ranking and prioritizing intersections for further analysis.

RECENT RESULTS IN HIERARCHICAL CLUSTERING: I–THE REDUCIBLE NEIGHBORHOODS CLUSTERING ALGORITHM

1993 ◽  
Vol 07 (03) ◽  
pp. 541-571 ◽  
Author(s):  
MICHEL BRUYNOOGHE
Keyword(s):  
Hierarchical Clustering ◽  
Speech Processing ◽  
Clustering Algorithm ◽  
Large Data ◽  
Original Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Set ◽  
Hierarchical Clustering Algorithm ◽  
Better Than

The clustering of large data sets is of great interest in fields such as pattern recognition, numerical taxonomy, image or speech processing. The traditional Ascendant Hierarchical Algorithm (AHC) cannot be run for sets of more than a few thousand elements. The reducible neighborhoods clustering algorithm, which is presented in this paper, has overtaken the limits of the traditional hierarchical clustering algorithm by generating an exact hierarchy on a large data set. The theoretical justification of this algorithm is the so-called Bruynooghe reducibility principle, that lays down the condition under which the exact hierarchy may be constructed locally, by carrying out aggregations in restricted regions of the representation space. As for the Day and Edelsbrunner algorithm, the maximum theoretical time complexity of the reducible neighborhoods clustering algorithm is O(n2 log n), regardless of the chosen clustering strategy. But the reducible neighborhoods clustering algorithm uses the original data table and its practical performances are by far better than Day and Edelsbrunner’s algorithm, thus allowing the hierarchical clustering of large data sets, i.e. composed of more than 10 000 objects.

scholarly journals Generation of geometric interpolations of building types with deep variational autoencoders

2020 ◽  
Vol 6 ◽  
Author(s):  
Jaime de Miguel Rodríguez ◽  
Maria Eugenia Villafañe ◽  
Luka Piškorec ◽  
Fernando Sancho Caparrini
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Large Data ◽  
Learning Model ◽  
Large Data Sets ◽  
Data Sets ◽  
Connectivity Map ◽  
Data Set ◽  
3D Objects ◽  
Machine Learning Model

Abstract This work presents a methodology for the generation of novel 3D objects resembling wireframes of building types. These result from the reconstruction of interpolated locations within the learnt distribution of variational autoencoders (VAEs), a deep generative machine learning model based on neural networks. The data set used features a scheme for geometry representation based on a ‘connectivity map’ that is especially suited to express the wireframe objects that compose it. Additionally, the input samples are generated through ‘parametric augmentation’, a strategy proposed in this study that creates coherent variations among data by enabling a set of parameters to alter representative features on a given building type. In the experiments that are described in this paper, more than 150 k input samples belonging to two building types have been processed during the training of a VAE model. The main contribution of this paper has been to explore parametric augmentation for the generation of large data sets of 3D geometries, showcasing its problems and limitations in the context of neural networks and VAEs. Results show that the generation of interpolated hybrid geometries is a challenging task. Despite the difficulty of the endeavour, promising advances are presented.

Effects of genotype and lactation number on health and reproductive problems in dairy cows

1997 ◽  
Vol 1997 ◽  
pp. 143-143
Author(s):  
B.L. Nielsen ◽  
R.F. Veerkamp ◽  
J.E. Pryce ◽  
G. Simm ◽  
J.D. Oldham
Keyword(s):  
Dairy Cows ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Variation Analysis ◽  
Genetic Line ◽  
Data Set ◽  
Health Events ◽  
Use Of Data ◽  
Low Incidence

High producing dairy cows have been found to be more susceptible to disease (Jones et al., 1994; Göhn et al., 1995) raising concerns about the welfare of the modern dairy cow. Genotype and number of lactations may affect various health problems differently, and their relative importance may vary. The categorical nature and low incidence of health events necessitates large data-sets, but the use of data collected across herds may introduce unwanted variation. Analysis of a comprehensive data-set from a single herd was carried out to investigate the effects of genetic line and lactation number on the incidence of various health and reproductive problems.

Scalable Non-Parametric Methods for Large Data Sets

2011 ◽  
pp. 1708-1713
Author(s):  
V. Suresh Babu ◽  
P. Viswanath ◽  
Narasimha M. Murty
Keyword(s):  
Nearest Neighbor ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Parametric Methods ◽  
Clustering Method ◽  
Data Set ◽  
Computational Burden ◽  
Set Size ◽  
Non Parametric

Non-parametric methods like the nearest neighbor classifier (NNC) and the Parzen-Window based density estimation (Duda, Hart & Stork, 2000) are more general than parametric methods because they do not make any assumptions regarding the probability distribution form. Further, they show good performance in practice with large data sets. These methods, either explicitly or implicitly estimates the probability density at a given point in a feature space by counting the number of points that fall in a small region around the given point. Popular classifiers which use this approach are the NNC and its variants like the k-nearest neighbor classifier (k-NNC) (Duda, Hart & Stock, 2000). Whereas the DBSCAN is a popular density based clustering method (Han & Kamber, 2001) which uses this approach. These methods show good performance, especially with larger data sets. Asymptotic error rate of NNC is less than twice the Bayes error (Cover & Hart, 1967) and DBSCAN can find arbitrary shaped clusters along with noisy outlier detection (Ester, Kriegel & Xu, 1996). The most prominent difficulty in applying the non-parametric methods for large data sets is its computational burden. The space and classification time complexities of NNC and k-NNC are O(n) where n is the training set size. The time complexity of DBSCAN is O(n2). So, these methods are not scalable for large data sets. Some of the remedies to reduce this burden are as follows. (1) Reduce the training set size by some editing techniques in order to eliminate some of the training patterns which are redundant in some sense (Dasarathy, 1991). For example, the condensed NNC (Hart, 1968) is of this type. (2) Use only a few selected prototypes from the data set. For example, Leaders-subleaders method and l-DBSCAN method are of this type (Vijaya, Murthy & Subramanian, 2004 and Viswanath & Rajwala, 2006). These two remedies can reduce the computational burden, but this can also result in a poor performance of the method. Using enriched prototypes can improve the performance as done in (Asharaf & Murthy, 2003) where the prototypes are derived using adaptive rough fuzzy set theory and as in (Suresh Babu & Viswanath, 2007) where the prototypes are used along with their relative weights. Using a few selected prototypes can reduce the computational burden. Prototypes can be derived by employing a clustering method like the leaders method (Spath, 1980), the k-means method (Jain, Dubes, & Chen, 1987), etc., which can find a partition of the data set where each block (cluster) of the partition is represented by a prototype called leader, centroid, etc. But these prototypes can not be used to estimate the probability density, since the density information present in the data set is lost while deriving the prototypes. The chapter proposes to use a modified leader clustering method called the counted-leader method which along with deriving the leaders preserves the crucial density information in the form of a count which can be used in estimating the densities. The chapter presents a fast and efficient nearest prototype based classifier called the counted k-nearest leader classifier (ck-NLC) which is on-par with the conventional k-NNC, but is considerably faster than the k-NNC. The chapter also presents a density based clustering method called l-DBSCAN which is shown to be a faster and scalable version of DBSCAN (Viswanath & Rajwala, 2006). Formally, under some assumptions, it is shown that the number of leaders is upper-bounded by a constant which is independent of the data set size and the distribution from which the data set is drawn.

scholarly journals GAUSSPY+: A fully automated Gaussian decomposition package for emission line spectra

2019 ◽  
Vol 628 ◽  
pp. A78 ◽  
Author(s):  
M. Riener ◽  
J. Kainulainen ◽  
J. D. Henshaw ◽  
J. H. Orkisz ◽  
C. E. Murray ◽  
...  
Keyword(s):  
Emission Line ◽  
Velocity Structure ◽  
Galactic Plane ◽  
Spatial Coherence ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Test Field ◽  
Data Set ◽  
Gaussian Decomposition

Our understanding of the dynamics of the interstellar medium is informed by the study of the detailed velocity structure of emission line observations. One approach to study the velocity structure is to decompose the spectra into individual velocity components; this leads to a description of the data set that is significantly reduced in complexity. However, this decomposition requires full automation lest it become prohibitive for large data sets, such as Galactic plane surveys. We developed GAUSSPY+, a fully automated Gaussian decomposition package that can be applied to emission line data sets, especially large surveys of HI and isotopologues of CO. We built our package upon the existing GAUSSPY algorithm and significantly improved its performance for noisy data. New functionalities of GAUSSPY+ include: (i) automated preparatory steps, such as an accurate noise estimation, which can also be used as stand-alone applications; (ii) an improved fitting routine; (iii) an automated spatial refitting routine that can add spatial coherence to the decomposition results by refitting spectra based on neighbouring fit solutions. We thoroughly tested the performance of GAUSSPY+ on synthetic spectra and a test field from the Galactic Ring Survey. We found that GAUSSPY+ can deal with cases of complex emission and even low to moderate signal-to-noise values.

scholarly journals Data segmentation based on the local intrinsic dimension

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Michele Allegra ◽  
Elena Facco ◽  
Francesco Denti ◽  
Alessandro Laio ◽  
Antonietta Mira
Keyword(s):  
High Dimensional Data ◽  
Large Data ◽  
Large Data Sets ◽  
High Dimensional ◽  
Data Sets ◽  
Imaging Data ◽  
Unsupervised Segmentation ◽  
Real World Data ◽  
Data Set ◽  
Intrinsic Dimension

Abstract One of the founding paradigms of machine learning is that a small number of variables is often sufficient to describe high-dimensional data. The minimum number of variables required is called the intrinsic dimension (ID) of the data. Contrary to common intuition, there are cases where the ID varies within the same data set. This fact has been highlighted in technical discussions, but seldom exploited to analyze large data sets and obtain insight into their structure. Here we develop a robust approach to discriminate regions with different local IDs and segment the points accordingly. Our approach is computationally efficient and can be proficiently used even on large data sets. We find that many real-world data sets contain regions with widely heterogeneous dimensions. These regions host points differing in core properties: folded versus unfolded configurations in a protein molecular dynamics trajectory, active versus non-active regions in brain imaging data, and firms with different financial risk in company balance sheets. A simple topological feature, the local ID, is thus sufficient to achieve an unsupervised segmentation of high-dimensional data, complementary to the one given by clustering algorithms.

Machine Learning Methods for Demand Estimation

2015 ◽  
Vol 105 (5) ◽  
pp. 481-485 ◽  
Author(s):  
Patrick Bajari ◽  
Denis Nekipelov ◽  
Stephen P. Ryan ◽  
Miaoyu Yang
Keyword(s):  
Large Data ◽  
Demand Estimation ◽  
Large Data Sets ◽  
Linear Functions ◽  
Data Sets ◽  
Science Literature ◽  
Data Set ◽  
Out Of Sample ◽  
Out Of Sample Prediction ◽  
Scanner Panel

We survey and apply several techniques from the statistical and computer science literature to the problem of demand estimation. To improve out-of-sample prediction accuracy, we propose a method of combining the underlying models via linear regression. Our method is robust to a large number of regressors; scales easily to very large data sets; combines model selection and estimation; and can flexibly approximate arbitrary non-linear functions. We illustrate our method using a standard scanner panel data set and find that our estimates are considerably more accurate in out-of-sample predictions of demand than some commonly used alternatives.

scholarly journals A review of forecasting techniques for large datasets

2008 ◽  
Vol 203 ◽  
pp. 109-115 ◽  
Author(s):  
Jana Eklund ◽  
George Kapetanios
Keyword(s):  
State Of The Art ◽  
Large Data ◽  
Large Datasets ◽  
Large Data Sets ◽  
Multiple Models ◽  
Small Subset ◽  
Data Sets ◽  
Single Model ◽  
Data Set ◽  
Forecasting Techniques

This paper aims to provide a brief and relatively non-technical overview of state-of-the-art forecasting with large data sets. We classify existing methods into four groups depending on whether data sets are used wholly or partly, whether a single model or multiple models are used and whether a small subset or the whole data set is being forecast. In particular, we provide brief descriptions of the methods and short recommendations where appropriate, without going into detailed discussions of their merits or demerits.

scholarly journals A dynamic K-means clustering for data mining

2019 ◽  
Vol 13 (2) ◽  
pp. 521
Author(s):  
Md. Zakir Hossain ◽  
Md.Nasim Akhtar ◽  
R.B. Ahmad ◽  
Mostafijur Rahman
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Large Data ◽  
Threshold Value ◽  
Specific Pattern ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Set ◽  
Number Of Clusters ◽  
Data Points

<span>Data mining is the process of finding structure of data from large data sets. With this process, the decision makers can make a particular decision for further development of the real-world problems. Several data clusteringtechniques are used in data mining for finding a specific pattern of data. The K-means method isone of the familiar clustering techniques for clustering large data sets.  The K-means clustering method partitions the data set based on the assumption that the number of clusters are fixed.The main problem of this method is that if the number of clusters is to be chosen small then there is a higher probability of adding dissimilar items into the same group. On the other hand, if the number of clusters is chosen to be high, then there is a higher chance of adding similar items in the different groups. In this paper, we address this issue by proposing a new K-Means clustering algorithm. The proposed method performs data clustering dynamically. The proposed method initially calculates a threshold value as a centroid of K-Means and based on this value the number of clusters are formed. At each iteration of K-Means, if the Euclidian distance between two points is less than or equal to the threshold value, then these two data points will be in the same group. Otherwise, the proposed method will create a new cluster with the dissimilar data point. The results show that the proposed method outperforms the original K-Means method.</span>

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