307,271 research outputs found
Web Mining Research: A Survey
With the huge amount of information available online, the World Wide Web is a
fertile area for data mining research. The Web mining research is at the cross
road of research from several research communities, such as database,
information retrieval, and within AI, especially the sub-areas of machine
learning and natural language processing. However, there is a lot of confusions
when comparing research efforts from different point of views. In this paper,
we survey the research in the area of Web mining, point out some confusions
regarded the usage of the term Web mining and suggest three Web mining
categories. Then we situate some of the research with respect to these three
categories. We also explore the connection between the Web mining categories
and the related agent paradigm. For the survey, we focus on representation
issues, on the process, on the learning algorithm, and on the application of
the recent works as the criteria. We conclude the paper with some research
issues.Comment: 15 page
Application of Visual Clustering Properties of Self Organizing Map in Machine-part Cell Formation
Cellular manufacturing (CM) is an approach that includes both flexibility of
job shops and high production rate of flow lines. Although CM provides many
benefits in reducing throughput times, setup times, work-in-process inventories
but the design of CM is complex and NP complete problem. The cell formation
problem based on operation sequence (ordinal data) is rarely reported in the
literature. The objective of the present paper is to propose a visual
clustering approach for machine-part cell formation using Self Organizing Map
(SOM) algorithm an unsupervised neural network to achieve better group
technology efficiency measure of cell formation as well as measure of SOM
quality. The work also has established the criteria of choosing an optimum SOM
map size based on results of quantization error, topography error, and average
distortion measure during SOM training which have generated the best clustering
and preservation of topology. To evaluate the performance of the proposed
algorithm, we tested the several benchmark problems available in the
literature. The results show that the proposed approach not only generates the
best and accurate solution as any of the results reported, so far, in
literature but also, in some instances the results produced are even better
than the previously reported results. The effectiveness of the proposed
approach is also statistically verified.Comment: 33 pages, 7 figures, 7 table
On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models
This paper addresses the general problem of reinforcement learning (RL) in
partially observable environments. In 2013, our large RL recurrent neural
networks (RNNs) learned from scratch to drive simulated cars from
high-dimensional video input. However, real brains are more powerful in many
ways. In particular, they learn a predictive model of their initially unknown
environment, and somehow use it for abstract (e.g., hierarchical) planning and
reasoning. Guided by algorithmic information theory, we describe RNN-based AIs
(RNNAIs) designed to do the same. Such an RNNAI can be trained on never-ending
sequences of tasks, some of them provided by the user, others invented by the
RNNAI itself in a curious, playful fashion, to improve its RNN-based world
model. Unlike our previous model-building RNN-based RL machines dating back to
1990, the RNNAI learns to actively query its model for abstract reasoning and
planning and decision making, essentially "learning to think." The basic ideas
of this report can be applied to many other cases where one RNN-like system
exploits the algorithmic information content of another. They are taken from a
grant proposal submitted in Fall 2014, and also explain concepts such as
"mirror neurons." Experimental results will be described in separate papers.Comment: 36 pages, 1 figure. arXiv admin note: substantial text overlap with
arXiv:1404.782
Nonnegative Restricted Boltzmann Machines for Parts-based Representations Discovery and Predictive Model Stabilization
The success of any machine learning system depends critically on effective
representations of data. In many cases, it is desirable that a representation
scheme uncovers the parts-based, additive nature of the data. Of current
representation learning schemes, restricted Boltzmann machines (RBMs) have
proved to be highly effective in unsupervised settings. However, when it comes
to parts-based discovery, RBMs do not usually produce satisfactory results. We
enhance such capacity of RBMs by introducing nonnegativity into the model
weights, resulting in a variant called nonnegative restricted Boltzmann machine
(NRBM). The NRBM produces not only controllable decomposition of data into
interpretable parts but also offers a way to estimate the intrinsic nonlinear
dimensionality of data, and helps to stabilize linear predictive models. We
demonstrate the capacity of our model on applications such as handwritten digit
recognition, face recognition, document classification and patient readmission
prognosis. The decomposition quality on images is comparable with or better
than what produced by the nonnegative matrix factorization (NMF), and the
thematic features uncovered from text are qualitatively interpretable in a
similar manner to that of the latent Dirichlet allocation (LDA). The stability
performance of feature selection on medical data is better than RBM and
competitive with NMF. The learned features, when used for classification, are
more discriminative than those discovered by both NMF and LDA and comparable
with those by RBM
Subsurface structure analysis using computational interpretation and learning: A visual signal processing perspective
Understanding Earth's subsurface structures has been and continues to be an
essential component of various applications such as environmental monitoring,
carbon sequestration, and oil and gas exploration. By viewing the seismic
volumes that are generated through the processing of recorded seismic traces,
researchers were able to learn from applying advanced image processing and
computer vision algorithms to effectively analyze and understand Earth's
subsurface structures. In this paper, first, we summarize the recent advances
in this direction that relied heavily on the fields of image processing and
computer vision. Second, we discuss the challenges in seismic interpretation
and provide insights and some directions to address such challenges using
emerging machine learning algorithms
Artificial intelligence and pediatrics: A synthetic mini review
The use of artificial intelligence intelligencein medicine can be traced back
to 1968 when Paycha published his paper Le diagnostic a l'aide d'intelligences
artificielle, presentation de la premiere machine diagnostri. Few years later
Shortliffe et al. presented an expert system named Mycin which was able to
identify bacteria causing severe blood infections and to recommend antibiotics.
Despite the fact that Mycin outperformed members of the Stanford medical school
in the reliability of diagnosis it was never used in practice due to a legal
issue who do you sue if it gives a wrong diagnosis?. However only in 2016 when
the artificial intelligence software built into the IBM Watson AI platform
correctly diagnosed and proposed an effective treatment for a 60-year-old
womans rare form of leukemia the AI use in medicine become really popular.On of
first papers presenting the use of AI in paediatrics was published in 1984. The
paper introduced a computer-assisted medical decision making system called
SHELP
An Outlier Detection-based Tree Selection Approach to Extreme Pruning of Random Forests
Random Forest (RF) is an ensemble classification technique that was developed
by Breiman over a decade ago. Compared with other ensemble techniques, it has
proved its accuracy and superiority. Many researchers, however, believe that
there is still room for enhancing and improving its performance in terms of
predictive accuracy. This explains why, over the past decade, there have been
many extensions of RF where each extension employed a variety of techniques and
strategies to improve certain aspect(s) of RF. Since it has been proven
empirically that ensembles tend to yield better results when there is a
significant diversity among the constituent models, the objective of this paper
is twofolds. First, it investigates how an unsupervised learning technique,
namely, Local Outlier Factor (LOF) can be used to identify diverse trees in the
RF. Second, trees with the highest LOF scores are then used to produce an
extension of RF termed LOFB-DRF that is much smaller in size than RF, and yet
performs at least as good as RF, but mostly exhibits higher performance in
terms of accuracy. The latter refers to a known technique called ensemble
pruning. Experimental results on 10 real datasets prove the superiority of our
proposed extension over the traditional RF. Unprecedented pruning levels
reaching 99% have been achieved at the time of boosting the predictive accuracy
of the ensemble. The notably high pruning level makes the technique a good
candidate for real-time applications.Comment: 21 pages, 4 Figures. arXiv admin note: substantial text overlap with
arXiv:1503.0499
Multi-parametric Solution-path Algorithm for Instance-weighted Support Vector Machines
An instance-weighted variant of the support vector machine (SVM) has
attracted considerable attention recently since they are useful in various
machine learning tasks such as non-stationary data analysis, heteroscedastic
data modeling, transfer learning, learning to rank, and transduction. An
important challenge in these scenarios is to overcome the computational
bottleneck---instance weights often change dynamically or adaptively, and thus
the weighted SVM solutions must be repeatedly computed. In this paper, we
develop an algorithm that can efficiently and exactly update the weighted SVM
solutions for arbitrary change of instance weights. Technically, this
contribution can be regarded as an extension of the conventional solution-path
algorithm for a single regularization parameter to multiple instance-weight
parameters. However, this extension gives rise to a significant problem that
breakpoints (at which the solution path turns) have to be identified in
high-dimensional space. To facilitate this, we introduce a parametric
representation of instance weights. We also provide a geometric interpretation
in weight space using a notion of critical region: a polyhedron in which the
current affine solution remains to be optimal. Then we find breakpoints at
intersections of the solution path and boundaries of polyhedrons. Through
extensive experiments on various practical applications, we demonstrate the
usefulness of the proposed algorithm.Comment: Submitted to Journal of Machine Learning Researc
Machine Learning pipeline for discovering neuroimaging-based biomarkers in neurology and psychiatry
We consider a problem of diagnostic pattern recognition/classification from
neuroimaging data. We propose a common data analysis pipeline for
neuroimaging-based diagnostic classification problems using various ML
algorithms and processing toolboxes for brain imaging. We illustrate the
pipeline application by discovering new biomarkers for diagnostics of epilepsy
and depression based on clinical and MRI/fMRI data for patients and healthy
volunteers.Comment: 20 pages, 2 figure
Solving the "false positives" problem in fraud prediction
In this paper, we present an automated feature engineering based approach to
dramatically reduce false positives in fraud prediction. False positives plague
the fraud prediction industry. It is estimated that only 1 in 5 declared as
fraud are actually fraud and roughly 1 in every 6 customers have had a valid
transaction declined in the past year. To address this problem, we use the Deep
Feature Synthesis algorithm to automatically derive behavioral features based
on the historical data of the card associated with a transaction. We generate
237 features (>100 behavioral patterns) for each transaction, and use a random
forest to learn a classifier. We tested our machine learning model on data from
a large multinational bank and compared it to their existing solution. On an
unseen data of 1.852 million transactions, we were able to reduce the false
positives by 54% and provide a savings of 190K euros. We also assess how to
deploy this solution, and whether it necessitates streaming computation for
real time scoring. We found that our solution can maintain similar benefits
even when historical features are computed once every 7 days
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