22,870 research outputs found
Identifying Real Estate Opportunities using Machine Learning
The real estate market is exposed to many fluctuations in prices because of
existing correlations with many variables, some of which cannot be controlled
or might even be unknown. Housing prices can increase rapidly (or in some
cases, also drop very fast), yet the numerous listings available online where
houses are sold or rented are not likely to be updated that often. In some
cases, individuals interested in selling a house (or apartment) might include
it in some online listing, and forget about updating the price. In other cases,
some individuals might be interested in deliberately setting a price below the
market price in order to sell the home faster, for various reasons. In this
paper, we aim at developing a machine learning application that identifies
opportunities in the real estate market in real time, i.e., houses that are
listed with a price substantially below the market price. This program can be
useful for investors interested in the housing market. We have focused in a use
case considering real estate assets located in the Salamanca district in Madrid
(Spain) and listed in the most relevant Spanish online site for home sales and
rentals. The application is formally implemented as a regression problem that
tries to estimate the market price of a house given features retrieved from
public online listings. For building this application, we have performed a
feature engineering stage in order to discover relevant features that allows
for attaining a high predictive performance. Several machine learning
algorithms have been tested, including regression trees, k-nearest neighbors,
support vector machines and neural networks, identifying advantages and
handicaps of each of them.Comment: 24 pages, 13 figures, 5 table
Coupled Ensembles of Neural Networks
We investigate in this paper the architecture of deep convolutional networks.
Building on existing state of the art models, we propose a reconfiguration of
the model parameters into several parallel branches at the global network
level, with each branch being a standalone CNN. We show that this arrangement
is an efficient way to significantly reduce the number of parameters without
losing performance or to significantly improve the performance with the same
level of performance. The use of branches brings an additional form of
regularization. In addition to the split into parallel branches, we propose a
tighter coupling of these branches by placing the "fuse (averaging) layer"
before the Log-Likelihood and SoftMax layers during training. This gives
another significant performance improvement, the tighter coupling favouring the
learning of better representations, even at the level of the individual
branches. We refer to this branched architecture as "coupled ensembles". The
approach is very generic and can be applied with almost any DCNN architecture.
With coupled ensembles of DenseNet-BC and parameter budget of 25M, we obtain
error rates of 2.92%, 15.68% and 1.50% respectively on CIFAR-10, CIFAR-100 and
SVHN tasks. For the same budget, DenseNet-BC has error rate of 3.46%, 17.18%,
and 1.8% respectively. With ensembles of coupled ensembles, of DenseNet-BC
networks, with 50M total parameters, we obtain error rates of 2.72%, 15.13% and
1.42% respectively on these tasks
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Neurons and symbols: a manifesto
We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of
neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty
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