837,495 research outputs found
Beyond Covariation: Cues to Causal Structure
Causal induction has two components: learning about the structure of causal models and learning about causal strength and other quantitative parameters. This chapter argues for several interconnected theses. First, people represent causal knowledge qualitatively, in terms of causal structure; quantitative knowledge is derivative. Second, people use a variety of cues to infer causal structure aside from statistical data (e.g. temporal order, intervention, coherence with prior knowledge). Third, once a structural model is hypothesized, subsequent statistical data are used to confirm, refute, or elaborate the model. Fourth, people are limited in the number and complexity of causal models that they can hold in mind to test, but they can separately learn and then integrate simple models, and revise models by adding and removing single links. Finally, current computational models of learning need further development before they can be applied to human learning
Insurance: an R-Program to Model Insurance Data
Data sets from car insurance companies often have a high-dimensional complex dependency structure. The use of classical statistical methods such as generalized linear models or Tweedie?s compound Poisson model can yield problems in this case. Christmann (2004) proposed a general approach to model the pure premium by exploiting characteristic features of such data sets. In this paper we describe a program to use this approach based on a combination of multinomial logistic regression and [epsilon]-support vector regression from modern statistical machine learning. --Claim size,insurance tariff,logistic regression,statistical machine learning,support vector regression
Improving Malware Detection Accuracy by Extracting Icon Information
Detecting PE malware files is now commonly approached using statistical and
machine learning models. While these models commonly use features extracted
from the structure of PE files, we propose that icons from these files can also
help better predict malware. We propose an innovative machine learning approach
to extract information from icons. Our proposed approach consists of two steps:
1) extracting icon features using summary statics, histogram of gradients
(HOG), and a convolutional autoencoder, 2) clustering icons based on the
extracted icon features. Using publicly available data and by using machine
learning experiments, we show our proposed icon clusters significantly boost
the efficacy of malware prediction models. In particular, our experiments show
an average accuracy increase of 10% when icon clusters are used in the
prediction model.Comment: Full version. IEEE MIPR 201
Statistical learnability of nuclear masses
After more than 80 years from the seminal work of Weizs\"acker and the liquid
drop model of the atomic nucleus, deviations from experiments of mass models
( MeV) are orders of magnitude larger than experimental errors
( keV). Predicting the mass of atomic nuclei with precision is
extremely challenging. This is due to the non--trivial many--body interplay of
protons and neutrons in nuclei, and the complex nature of the nuclear strong
force. Statistical theory of learning will be used to provide bounds to the
prediction errors of model trained with a finite data set. These bounds are
validated with neural network calculations, and compared with state of the art
mass models. Therefore, it will be argued that the nuclear structure models
investigating ground state properties explore a system on the limit of the
knowledgeable, as defined by the statistical theory of learning
Identifying dynamical systems with bifurcations from noisy partial observation
Dynamical systems are used to model a variety of phenomena in which the
bifurcation structure is a fundamental characteristic. Here we propose a
statistical machine-learning approach to derive lowdimensional models that
automatically integrate information in noisy time-series data from partial
observations. The method is tested using artificial data generated from two
cell-cycle control system models that exhibit different bifurcations, and the
learned systems are shown to robustly inherit the bifurcation structure.Comment: 16 pages, 6 figure
On Graphical Models via Univariate Exponential Family Distributions
Undirected graphical models, or Markov networks, are a popular class of
statistical models, used in a wide variety of applications. Popular instances
of this class include Gaussian graphical models and Ising models. In many
settings, however, it might not be clear which subclass of graphical models to
use, particularly for non-Gaussian and non-categorical data. In this paper, we
consider a general sub-class of graphical models where the node-wise
conditional distributions arise from exponential families. This allows us to
derive multivariate graphical model distributions from univariate exponential
family distributions, such as the Poisson, negative binomial, and exponential
distributions. Our key contributions include a class of M-estimators to fit
these graphical model distributions; and rigorous statistical analysis showing
that these M-estimators recover the true graphical model structure exactly,
with high probability. We provide examples of genomic and proteomic networks
learned via instances of our class of graphical models derived from Poisson and
exponential distributions.Comment: Journal of Machine Learning Researc
Structure learning of antiferromagnetic Ising models
In this paper we investigate the computational complexity of learning the
graph structure underlying a discrete undirected graphical model from i.i.d.
samples. We first observe that the notoriously difficult problem of learning
parities with noise can be captured as a special case of learning graphical
models. This leads to an unconditional computational lower bound of for learning general graphical models on nodes of maximum degree
, for the class of so-called statistical algorithms recently introduced by
Feldman et al (2013). The lower bound suggests that the runtime
required to exhaustively search over neighborhoods cannot be significantly
improved without restricting the class of models.
Aside from structural assumptions on the graph such as it being a tree,
hypertree, tree-like, etc., many recent papers on structure learning assume
that the model has the correlation decay property. Indeed, focusing on
ferromagnetic Ising models, Bento and Montanari (2009) showed that all known
low-complexity algorithms fail to learn simple graphs when the interaction
strength exceeds a number related to the correlation decay threshold. Our
second set of results gives a class of repelling (antiferromagnetic) models
that have the opposite behavior: very strong interaction allows efficient
learning in time . We provide an algorithm whose performance
interpolates between and depending on the strength of the
repulsion.Comment: 15 pages. NIPS 201
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