7,913 research outputs found
Mapping cognitive ontologies to and from the brain
Imaging neuroscience links brain activation maps to behavior and cognition
via correlational studies. Due to the nature of the individual experiments,
based on eliciting neural response from a small number of stimuli, this link is
incomplete, and unidirectional from the causal point of view. To come to
conclusions on the function implied by the activation of brain regions, it is
necessary to combine a wide exploration of the various brain functions and some
inversion of the statistical inference. Here we introduce a methodology for
accumulating knowledge towards a bidirectional link between observed brain
activity and the corresponding function. We rely on a large corpus of imaging
studies and a predictive engine. Technically, the challenges are to find
commonality between the studies without denaturing the richness of the corpus.
The key elements that we contribute are labeling the tasks performed with a
cognitive ontology, and modeling the long tail of rare paradigms in the corpus.
To our knowledge, our approach is the first demonstration of predicting the
cognitive content of completely new brain images. To that end, we propose a
method that predicts the experimental paradigms across different studies.Comment: NIPS (Neural Information Processing Systems), United States (2013
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
A Winnow-Based Approach to Context-Sensitive Spelling Correction
A large class of machine-learning problems in natural language require the
characterization of linguistic context. Two characteristic properties of such
problems are that their feature space is of very high dimensionality, and their
target concepts refer to only a small subset of the features in the space.
Under such conditions, multiplicative weight-update algorithms such as Winnow
have been shown to have exceptionally good theoretical properties. We present
an algorithm combining variants of Winnow and weighted-majority voting, and
apply it to a problem in the aforementioned class: context-sensitive spelling
correction. This is the task of fixing spelling errors that happen to result in
valid words, such as substituting "to" for "too", "casual" for "causal", etc.
We evaluate our algorithm, WinSpell, by comparing it against BaySpell, a
statistics-based method representing the state of the art for this task. We
find: (1) When run with a full (unpruned) set of features, WinSpell achieves
accuracies significantly higher than BaySpell was able to achieve in either the
pruned or unpruned condition; (2) When compared with other systems in the
literature, WinSpell exhibits the highest performance; (3) The primary reason
that WinSpell outperforms BaySpell is that WinSpell learns a better linear
separator; (4) When run on a test set drawn from a different corpus than the
training set was drawn from, WinSpell is better able than BaySpell to adapt,
using a strategy we will present that combines supervised learning on the
training set with unsupervised learning on the (noisy) test set.Comment: To appear in Machine Learning, Special Issue on Natural Language
Learning, 1999. 25 page
Sequential Empirical Bayes method for filtering dynamic spatiotemporal processes
We consider online prediction of a latent dynamic spatiotemporal process and
estimation of the associated model parameters based on noisy data. The problem
is motivated by the analysis of spatial data arriving in real-time and the
current parameter estimates and predictions are updated using the new data at a
fixed computational cost. Estimation and prediction is performed within an
empirical Bayes framework with the aid of Markov chain Monte Carlo samples.
Samples for the latent spatial field are generated using a sampling importance
resampling algorithm with a skewed-normal proposal and for the temporal
parameters using Gibbs sampling with their full conditionals written in terms
of sufficient quantities which are updated online. The spatial range parameter
is estimated by a novel online implementation of an empirical Bayes method,
called herein sequential empirical Bayes method. A simulation study shows that
our method gives similar results as an offline Bayesian method. We also find
that the skewed-normal proposal improves over the traditional Gaussian
proposal. The application of our method is demonstrated for online monitoring
of radiation after the Fukushima nuclear accident
Multiple instance learning for sequence data with across bag dependencies
In Multiple Instance Learning (MIL) problem for sequence data, the instances
inside the bags are sequences. In some real world applications such as
bioinformatics, comparing a random couple of sequences makes no sense. In fact,
each instance may have structural and/or functional relations with instances of
other bags. Thus, the classification task should take into account this across
bag relation. In this work, we present two novel MIL approaches for sequence
data classification named ABClass and ABSim. ABClass extracts motifs from
related instances and use them to encode sequences. A discriminative classifier
is then applied to compute a partial classification result for each set of
related sequences. ABSim uses a similarity measure to discriminate the related
instances and to compute a scores matrix. For both approaches, an aggregation
method is applied in order to generate the final classification result. We
applied both approaches to solve the problem of bacterial Ionizing Radiation
Resistance prediction. The experimental results of the presented approaches are
satisfactory
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