622,574 research outputs found
Active Learning with Statistical Models
For many types of machine learning algorithms, one can compute the
statistically `optimal' way to select training data. In this paper, we review
how optimal data selection techniques have been used with feedforward neural
networks. We then show how the same principles may be used to select data for
two alternative, statistically-based learning architectures: mixtures of
Gaussians and locally weighted regression. While the techniques for neural
networks are computationally expensive and approximate, the techniques for
mixtures of Gaussians and locally weighted regression are both efficient and
accurate. Empirically, we observe that the optimality criterion sharply
decreases the number of training examples the learner needs in order to achieve
good performance.Comment: See http://www.jair.org/ for any accompanying file
Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters
We present several methods towards construction of precursors, which show
great promise towards early predictions, of solar flare events in this paper. A
data pre-processing pipeline is built to extract useful data from multiple
sources, Geostationary Operational Environmental Satellites (GOES) and Solar
Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI), to prepare
inputs for machine learning algorithms. Two classification models are
presented: classification of flares from quiet times for active regions and
classification of strong versus weak flare events. We adopt deep learning
algorithms to capture both the spatial and temporal information from HMI
magnetogram data. Effective feature extraction and feature selection with raw
magnetogram data using deep learning and statistical algorithms enable us to
train classification models to achieve almost as good performance as using
active region parameters provided in HMI/Space-Weather HMI-Active Region Patch
(SHARP) data files. Case studies show a significant increase in the prediction
score around 20 hours before strong solar flare events
Model-Centric and Data-Centric Aspects of Active Learning for Neural Network Models
We study different data-centric and model-centric aspects of active learning
with neural network models. i) We investigate incremental and cumulative
training modes that specify how the currently labeled data are used for
training. ii) Neural networks are models with a large capacity. Thus, we study
how active learning depends on the number of epochs and neurons as well as the
choice of batch size. iii) We analyze in detail the behavior of query
strategies and their corresponding informativeness measures and accordingly
propose more efficient querying and active learning paradigms. iv) We perform
statistical analyses, e.g., on actively learned classes and test error
estimation, that reveal several insights about active learning
Volumetric segmentation of multiple basal ganglia structures
We present a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. Neighboring anatomical structures in the human brain exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models
on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities based on training data, we use a nonparametric multivariate kernel density estimation framework.
We combine these priors with data in a variational framework, and develop an active contour-based iterative segmentation algorithm. We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images. We compare our technique with existing methods and demonstrate the improvements it provides in terms of segmentation accuracy
A survey of statistical network models
Networks are ubiquitous in science and have become a focal point for
discussion in everyday life. Formal statistical models for the analysis of
network data have emerged as a major topic of interest in diverse areas of
study, and most of these involve a form of graphical representation.
Probability models on graphs date back to 1959. Along with empirical studies in
social psychology and sociology from the 1960s, these early works generated an
active network community and a substantial literature in the 1970s. This effort
moved into the statistical literature in the late 1970s and 1980s, and the past
decade has seen a burgeoning network literature in statistical physics and
computer science. The growth of the World Wide Web and the emergence of online
networking communities such as Facebook, MySpace, and LinkedIn, and a host of
more specialized professional network communities has intensified interest in
the study of networks and network data. Our goal in this review is to provide
the reader with an entry point to this burgeoning literature. We begin with an
overview of the historical development of statistical network modeling and then
we introduce a number of examples that have been studied in the network
literature. Our subsequent discussion focuses on a number of prominent static
and dynamic network models and their interconnections. We emphasize formal
model descriptions, and pay special attention to the interpretation of
parameters and their estimation. We end with a description of some open
problems and challenges for machine learning and statistics.Comment: 96 pages, 14 figures, 333 reference
Pengaruh Penerapan Model Pembelajaran Guided Note Taking (Gnt) Pada Materi Termokimia Terhadap Hasil Belajar Siswa Kelas XI IPA SMA Negeri 2 Pasangkayu
Students in the learning required to take an active role in the learning process . But in fact, there are still many conventional learning that found. This can have an impact on student learning outcomes. Therefore we need an appropriate learning modes and appropriate. One of the learning model that allows students to play an active role in learning is a model guided note taking. This study aims to determine the effect of application of learning models guided note taking on learning outcomes of students of class XI Science SMAN 2 Pasangkau school year 2013/2014. The research sample that is class XI IPA 2 as the class experiment with the number of students 25 people and XI IPA 1 as a control class with the number of students was 27 people. The data was collected using achievement test instruments. Testing research data using t-test statistical analysis of the parties to the prerequisite test was a test for normality and homogeneity tests. Average score of student learning outcomes using the guided note taking learning model was 66.14 while the average score of student learning outcomes that follow the conventional learning 52.5. Based on the analysis of statistical hypothesis t-test with the values obtained by the tcounted = 3.693 and ttable = 1.67. So, tcounted> ttable showing that Ho refused and H1 accepted. It can be concluded that the guided note taking learning model gives higher learning outcomes than conventional learning models
Coupled non-parametric shape and moment-based inter-shape pose priors for multiple basal ganglia structure segmentation
This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm.
We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images.
We present a set of 2D and 3D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy
Statistical Hardware Design With Multi-model Active Learning
With the rising complexity of numerous novel applications that serve our
modern society comes the strong need to design efficient computing platforms.
Designing efficient hardware is, however, a complex multi-objective problem
that deals with multiple parameters and their interactions. Given that there
are a large number of parameters and objectives involved in hardware design,
synthesizing all possible combinations is not a feasible method to find the
optimal solution. One promising approach to tackle this problem is statistical
modeling of a desired hardware performance. Here, we propose a model-based
active learning approach to solve this problem. Our proposed method uses
Bayesian models to characterize various aspects of hardware performance. We
also use transfer learning and Gaussian regression bootstrapping techniques in
conjunction with active learning to create more accurate models. Our proposed
statistical modeling method provides hardware models that are sufficiently
accurate to perform design space exploration as well as performance prediction
simultaneously. We use our proposed method to perform design space exploration
and performance prediction for various hardware setups, such as
micro-architecture design and OpenCL kernels for FPGA targets. Our experiments
show that the number of samples required to create performance models
significantly reduces while maintaining the predictive power of our proposed
statistical models. For instance, in our performance prediction setting, the
proposed method needs 65% fewer samples to create the model, and in the design
space exploration setting, our proposed method can find the best parameter
settings by exploring less than 50 samples.Comment: added a reference for GRP subsampling and corrected typo
Changes and classification in myocardial contractile function in the left ventricle following acute myocardial infarction
In this research, we hypothesized that novel biomechanical parameters are discriminative in patients following acute ST-segment elevation myocardial infarction (STEMI). To identify these biomechanical biomarkers and bring computational biomechanics âcloser to the clinicâ, we applied state-of-the-art multiphysics cardiac modelling combined with advanced machine learning and multivariate statistical inference to a clinical database of myocardial infarction. We obtained data from 11 STEMI patients (ClinicalTrials.gov NCT01717573) and 27 healthy volunteers, and developed personalized mathematical models for the left ventricle (LV) using an immersed boundary method. Subject-specific constitutive parameters were achieved by matching to clinical measurements. We have shown, for the first time, that compared with healthy controls, patients with STEMI exhibited increased LV wall active tension when normalized by systolic blood pressure, which suggests an increased demand on the contractile reserve of remote functional myocardium. The statistical analysis reveals that the required patient-specific contractility, normalized active tension and the systolic myofilament kinematics have the strongest explanatory power for identifying the myocardial function changes post-MI. We further observed a strong correlation between two biomarkers and the changes in LV ejection fraction at six months from baseline (the required contractility (r = â 0.79, p < 0.01) and the systolic myofilament kinematics (r = 0.70, p = 0.02)). The clinical and prognostic significance of these biomechanical parameters merits further scrutinization
Revealing Online Learning Behaviors and Activity Patterns and Making Predictions with Data Mining Techniques in Online Teaching
This study was conducted with data mining (DM) techniques to analyze various patterns of online learning behaviors, and to make predictions on learning outcomes. Statistical models and machine learning DM techniques were conducted to analyze 17,934 server logs to investigate 98 undergraduate studentsâ learning behaviors in an online business course in Taiwan. The study scientifically identified studentsâ behavioral patterns and preferences in the online learning processes, differentiated active and passive learners, and found important parameters for performance prediction. The results also demonstrated how data mining techniques might be utilized to help improve online teaching and learning with suggestions for online instructors, instructional designers and courseware developers
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