262 research outputs found
Estimating labels from label proportions
Consider the following problem: given sets of unlabeled observations, each set with known label proportions, predict the labels of another set of observations, also with known label proportions. This problem appears in areas like e-commerce, spam filtering and improper content detection. We present consistent estimators which can reconstruct the correct labels with high probability in a uniform convergence sense. Experiments show that our method works well in practice.
RIDI: Robust IMU Double Integration
This paper proposes a novel data-driven approach for inertial navigation,
which learns to estimate trajectories of natural human motions just from an
inertial measurement unit (IMU) in every smartphone. The key observation is
that human motions are repetitive and consist of a few major modes (e.g.,
standing, walking, or turning). Our algorithm regresses a velocity vector from
the history of linear accelerations and angular velocities, then corrects
low-frequency bias in the linear accelerations, which are integrated twice to
estimate positions. We have acquired training data with ground-truth motions
across multiple human subjects and multiple phone placements (e.g., in a bag or
a hand). The qualitatively and quantitatively evaluations have demonstrated
that our algorithm has surprisingly shown comparable results to full Visual
Inertial navigation. To our knowledge, this paper is the first to integrate
sophisticated machine learning techniques with inertial navigation, potentially
opening up a new line of research in the domain of data-driven inertial
navigation. We will publicly share our code and data to facilitate further
research
Multivariate dynamic kernels for financial time series forecasting
The final publication is available at http://link.springer.com/chapter/10.1007/978-3-319-44781-0_40We propose a forecasting procedure based on multivariate dynamic kernels, with the capability of integrating information measured at different frequencies and at irregular time intervals in financial markets. A data compression process redefines the original financial time series into temporal data blocks, analyzing the temporal information of multiple time intervals. The analysis is done through multivariate dynamic kernels within support vector regression. We also propose two kernels for financial time series that are computationally efficient without a sacrifice on accuracy. The efficacy of the methodology is demonstrated by empirical experiments on forecasting the challenging S&P500 market.Peer ReviewedPostprint (author's final draft
Deep Learning for Forecasting Stock Returns in the Cross-Section
Many studies have been undertaken by using machine learning techniques,
including neural networks, to predict stock returns. Recently, a method known
as deep learning, which achieves high performance mainly in image recognition
and speech recognition, has attracted attention in the machine learning field.
This paper implements deep learning to predict one-month-ahead stock returns in
the cross-section in the Japanese stock market and investigates the performance
of the method. Our results show that deep neural networks generally outperform
shallow neural networks, and the best networks also outperform representative
machine learning models. These results indicate that deep learning shows
promise as a skillful machine learning method to predict stock returns in the
cross-section.Comment: 12 pages, 2 figures, 8 tables, accepted at PAKDD 201
Robust artificial neural networks and outlier detection. Technical report
Large outliers break down linear and nonlinear regression models. Robust
regression methods allow one to filter out the outliers when building a model.
By replacing the traditional least squares criterion with the least trimmed
squares criterion, in which half of data is treated as potential outliers, one
can fit accurate regression models to strongly contaminated data.
High-breakdown methods have become very well established in linear regression,
but have started being applied for non-linear regression only recently. In this
work, we examine the problem of fitting artificial neural networks to
contaminated data using least trimmed squares criterion. We introduce a
penalized least trimmed squares criterion which prevents unnecessary removal of
valid data. Training of ANNs leads to a challenging non-smooth global
optimization problem. We compare the efficiency of several derivative-free
optimization methods in solving it, and show that our approach identifies the
outliers correctly when ANNs are used for nonlinear regression
A framework for space-efficient string kernels
String kernels are typically used to compare genome-scale sequences whose
length makes alignment impractical, yet their computation is based on data
structures that are either space-inefficient, or incur large slowdowns. We show
that a number of exact string kernels, like the -mer kernel, the substrings
kernels, a number of length-weighted kernels, the minimal absent words kernel,
and kernels with Markovian corrections, can all be computed in time and
in bits of space in addition to the input, using just a
data structure on the Burrows-Wheeler transform of the
input strings, which takes time per element in its output. The same
bounds hold for a number of measures of compositional complexity based on
multiple value of , like the -mer profile and the -th order empirical
entropy, and for calibrating the value of using the data
VIP-STB farm: scale-up village to county/province level to support science and technology at backyard (STB) program.
In this paper, we introduce a new concept in VIP-STB, a funded project through Agri-Tech in China: Newton Network+ (ATCNN), in developing feasible solutions towards scaling-up STB from village level to upper level via some generic models and systems. There are three tasks in this project, i.e. normalized difference vegetation index (NDVI) estimation, wheat density estimation and household-based small farms (HBSF) engagement. In the first task, several machine learning models have been used to evaluate the performance of NDVI estimation. In the second task, integrated software via Python and Twilio is developed to improve communication services and engagement for HBSFs, and provides technical capabilities. In the third task, crop density/population is predicted by conventional image processing techniques. The objectives and strategy for VIP-STB are described, experimental results on each task are presented, and more details on each model that has been implemented are also provided with future development guidance
Predicting sentence translation quality using extrinsic and language independent features
We develop a top performing model for automatic, accurate, and language independent prediction of sentence-level statistical machine translation (SMT) quality with or without looking at the translation outputs.
We derive various feature functions measuring the closeness of a given test sentence to the training data and
the difficulty of translating the sentence.
We describe \texttt{mono} feature functions that are based on statistics of only one side of the parallel
training corpora and \texttt{duo} feature functions that incorporate statistics involving both source and
target sides of the training data.
Overall, we describe novel, language independent, and SMT system extrinsic features for predicting the SMT performance, which also rank high during feature ranking evaluations.
We experiment with different learning settings, with or without looking at the translations, which help differentiate the contribution of different feature sets.
We apply partial least squares and feature subset selection, both of which improve the results and we present ranking of the top features selected for each learning setting, providing an exhaustive analysis of the extrinsic features used.
We show that by just looking at the test source sentences and not using the translation outputs at all, we can
achieve better performance than a baseline system using SMT model dependent features that generated the
translations.
Furthermore, our prediction system is able to achieve the nd best performance overall according to the official
results of the Quality Estimation Task (QET) challenge when also looking at the translation outputs.
Our representation and features achieve the top performance in QET among the models using the SVR learning model
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