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31,602 | DUGMA: Dynamic Uncertainty-Based Gaussian Mixture Alignment | cs.CV | Registering accurately point clouds from a cheap low-resolution sensor is a
challenging task. Existing rigid registration methods failed to use the
physical 3D uncertainty distribution of each point from a real sensor in the
dynamic alignment process mainly because the uncertainty model for a point is
static and invari... | computer science |
31,603 | Ocean Eddy Identification and Tracking using Neural Networks | cs.CV | Global climate change plays an essential role in our daily life and is
nowadays one of the most important topics. Mesoscale ocean eddies have a
significant impact on global warming, since they dominate the ocean dynamics,
the energy as well as the mass transports of ocean circulation. In particular,
from satellite alti... | computer science |
31,604 | LDOP: Local Directional Order Pattern for Robust Face Retrieval | cs.CV | The local descriptors have gained wide range of attention due to their
enhanced discriminative abilities. It has been proved that the consideration of
multi-scale local neighborhood improves the performance of the descriptor,
though at the cost of increased dimension. This paper proposes a novel method
to construct a l... | computer science |
31,605 | Speech-Driven Facial Reenactment Using Conditional Generative
Adversarial Networks | cs.CV | We present a novel approach to generating photo-realistic images of a face
with accurate lip sync, given an audio input. By using a recurrent neural
network, we achieved mouth landmarks based on audio features. We exploited the
power of conditional generative adversarial networks to produce
highly-realistic face condit... | computer science |
31,606 | VQA-E: Explaining, Elaborating, and Enhancing Your Answers for Visual
Questions | cs.CV | Most existing works in visual question answering (VQA) are dedicated to
improving the accuracy of predicted answers, while disregarding the
explanations. We argue that the explanation for an answer is of the same or
even more importance compared with the answer itself, since it makes the
question and answering process ... | computer science |
31,607 | MAGSAC: marginalizing sample consensus | cs.CV | A method called sigma-consensus is proposed to eliminate the need for a
user-defined inlier-outlier threshold in RANSAC. Instead of estimating sigma,
it is marginalized over a range of noise scales using a Bayesian estimator,
i.e. the optimized model is obtained as the weighted average using the
posterior probabilities... | computer science |
31,608 | An Improved Evaluation Framework for Generative Adversarial Networks | cs.CV | In this paper, we propose an improved quantitative evaluation framework for
Generative Adversarial Networks on generating domain-specific images, where we
improve conventional evaluation methods on two levels: the feature
representation and the evaluation metric. Unlike most existing evaluation
frameworks which transfe... | computer science |
31,609 | Actor and Action Video Segmentation from a Sentence | cs.CV | This paper strives for pixel-level segmentation of actors and their actions
in video content. Different from existing works, which all learn to segment
from a fixed vocabulary of actor and action pairs, we infer the segmentation
from a natural language input sentence. This allows to distinguish between
fine-grained act... | computer science |
31,610 | FastDeRain: A Novel Video Rain Streak Removal Method Using Directional
Gradient Priors | cs.CV | Rain streak removal is an important issue in outdoor vision systems and has
recently been investigated extensively. In this paper, we propose a novel video
rain streak removal approach FastDeRain, which fully considers the
discriminative characteristics of rain streaks and the clean video in the
gradient domain. Specif... | computer science |
31,611 | C3PO: Database and Benchmark for Early-stage Malicious Activity
Detection in 3D Printing | cs.CV | Increasing malicious users have sought practices to leverage 3D printing
technology to produce unlawful tools in criminal activities. Current
regulations are inadequate to deal with the rapid growth of 3D printers. It is
of vital importance to enable 3D printers to identify the objects to be
printed, so that the manufa... | computer science |
31,612 | Learning Category-Specific Mesh Reconstruction from Image Collections | cs.CV | We present a learning framework for recovering the 3D shape, camera, and
texture of an object from a single image. The shape is represented as a
deformable 3D mesh model of an object category where a shape is parameterized
by a learned mean shape and per-instance predicted deformation. Our approach
allows leveraging an... | computer science |
31,613 | Thermal to Visible Synthesis of Face Images using Multiple Regions | cs.CV | Synthesis of visible spectrum faces from thermal facial imagery is a
promising approach for heterogeneous face recognition; enabling existing face
recognition software trained on visible imagery to be leveraged, and allowing
human analysts to verify cross-spectrum matches more effectively. We propose a
new synthesis me... | computer science |
31,614 | A Feature-Driven Active Framework for Ultrasound-Based Brain Shift
Compensation | cs.CV | A reliable Ultrasound (US)-to-US registration method to compensate for brain
shift would substantially improve Image-Guided Neurological Surgery. Developing
such a registration method is very challenging, due to factors such as missing
correspondence in images, the complexity of brain pathology and the demand for
fast ... | computer science |
31,615 | Robust Depth Estimation from Auto Bracketed Images | cs.CV | As demand for advanced photographic applications on hand-held devices grows,
these electronics require the capture of high quality depth. However, under
low-light conditions, most devices still suffer from low imaging quality and
inaccurate depth acquisition. To address the problem, we present a robust depth
estimation... | computer science |
31,616 | Weakly Supervised Medical Diagnosis and Localization from Multiple
Resolutions | cs.CV | Diagnostic imaging often requires the simultaneous identification of a
multitude of findings of varied size and appearance. Beyond global indication
of said findings, the prediction and display of localization information
improves trust in and understanding of results when augmenting clinical
workflow. Medical training... | computer science |
31,617 | Generative Adversarial Talking Head: Bringing Portraits to Life with a
Weakly Supervised Neural Network | cs.CV | This paper presents Generative Adversarial Talking Head (GATH), a novel deep
generative neural network that enables fully automatic facial expression
synthesis of an arbitrary portrait with continuous action unit (AU)
coefficients. Specifically, our model directly manipulates image pixels to make
the unseen subject in ... | computer science |
31,618 | Modeling Camera Effects to Improve Deep Vision for Real and Synthetic
Data | cs.CV | Recent work has focused on generating synthetic imagery and augmenting real
imagery to increase the size and variability of training data for learning
visual tasks in urban scenes. This includes increasing the occurrence of
occlusions or varying environmental and weather effects. However, few have
addressed modeling th... | computer science |
31,619 | PyramidBox: A Context-assisted Single Shot Face Detector | cs.CV | Face detection has been well studied for many years and one of the remaining
challenges is to detect small, blurred and partially occluded faces in
uncontrolled environment. This paper proposes a novel context-assisted single
shot face detector, named PyramidBox, to handle the hard face detection
problem. Observing the... | computer science |
31,620 | Assessing Shape Bias Property of Convolutional Neural Networks | cs.CV | It is known that humans display "shape bias" when classifying new items,
i.e., they prefer to categorize objects based on their shape rather than color.
Convolutional Neural Networks (CNNs) are also designed to take into account the
spatial structure of image data. In fact, experiments on image datasets,
consisting of ... | computer science |
31,621 | Fast Semantic Segmentation on Video Using Motion Vector-Based Feature
Interpolation | cs.CV | Models optimized for accuracy on challenging, dense prediction tasks such as
semantic segmentation entail significant inference costs, and are prohibitively
slow to run on each frame in a video. Since nearby video frames are spatially
similar, however, there is substantial opportunity to reuse computation.
Existing wor... | computer science |
31,622 | Learning and Recognizing Human Action from Skeleton Movement with Deep
Residual Neural Networks | cs.CV | Automatic human action recognition is indispensable for almost artificial
intelligent systems such as video surveillance, human-computer interfaces,
video retrieval, etc. Despite a lot of progress, recognizing actions in an
unknown video is still a challenging task in computer vision. Recently, deep
learning algorithms... | computer science |
31,623 | Exploiting deep residual networks for human action recognition from
skeletal data | cs.CV | The computer vision community is currently focusing on solving action
recognition problems in real videos, which contain thousands of samples with
many challenges. In this process, Deep Convolutional Neural Networks (D-CNNs)
have played a significant role in advancing the state-of-the-art in various
vision-based action... | computer science |
31,624 | Domain Adaptation for Ear Recognition Using Deep Convolutional Neural
Networks | cs.CV | In this paper, we have extensively investigated the unconstrained ear
recognition problem. We have first shown the importance of domain adaptation,
when deep convolutional neural network models are used for ear recognition. To
enable domain adaptation, we have collected a new ear dataset using the
Multi-PIE face datase... | computer science |
31,625 | Patch-based Fake Fingerprint Detection Using a Fully Convolutional
Neural Network with a Small Number of Parameters and an Optimal Threshold | cs.CV | Fingerprint authentication is widely used in biometrics due to its simple
process, but it is vulnerable to fake fingerprints. This study proposes a
patch-based fake fingerprint detection method using a fully convolutional
neural network with a small number of parameters and an optimal threshold to
solve the above-menti... | computer science |
31,626 | End-to-End Fingerprints Liveness Detection using Convolutional Networks
with Gram module | cs.CV | This paper proposes an end-to-end CNN(Convolutional Neural Networks) model
that uses gram modules with parameters that are approximately 1.2MB in size to
detect fake fingerprints. The proposed method assumes that texture is the most
appropriate characteristic in fake fingerprint detection, and implements the
gram modul... | computer science |
31,627 | HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object
Classification | cs.CV | Event-based cameras have recently drawn the attention of the Computer Vision
community thanks to their advantages in terms of high temporal resolution, low
power consumption and high dynamic range, compared to traditional frame-based
cameras. These properties make event-based cameras an ideal choice for
autonomous vehi... | computer science |
31,628 | End-to-End Video Captioning with Multitask Reinforcement Learning | cs.CV | Although end-to-end (E2E) learning has led to promising performance on a
variety of tasks, it is often impeded by hardware constraints (e.g., GPU
memories) and is prone to overfitting. When it comes to video captioning, one
of the most challenging benchmark tasks in computer vision and machine
learning, those limitatio... | computer science |
31,629 | A Cascaded Convolutional Neural Network for Single Image Dehazing | cs.CV | Images captured under outdoor scenes usually suffer from low contrast and
limited visibility due to suspended atmospheric particles, which directly
affects the quality of photos. Despite numerous image dehazing methods have
been proposed, effective hazy image restoration remains a challenging problem.
Existing learning... | computer science |
31,630 | Non-rigid 3D Shape Registration using an Adaptive Template | cs.CV | We present a new fully-automatic non-rigid 3D shape registration (morphing)
framework comprising (1) a new 3D landmarking and pose normalisation method;
(2) an adaptive shape template method to accelerate the convergence of
registration algorithms and achieve a better final shape correspondence and (3)
a new iterative ... | computer science |
31,631 | BioTracker: An Open-Source Computer Vision Framework for Visual Animal
Tracking | cs.CV | The study of animal behavior increasingly relies on (semi-) automatic methods
for the extraction of relevant behavioral features from video or picture data.
To date, several specialized software products exist to detect and track
animals' positions in simple (laboratory) environments. Tracking animals in
their natural ... | computer science |
31,632 | Quantification of Lung Abnormalities in Cystic Fibrosis using Deep
Networks | cs.CV | Cystic fibrosis is a genetic disease which may appear in early life with
structural abnormalities in lung tissues. We propose to detect these
abnormalities using a texture classification approach. Our method is a cascade
of two convolutional neural networks. The first network detects the presence of
abnormal tissues. T... | computer science |
31,633 | Video Object Segmentation with Language Referring Expressions | cs.CV | Most state-of-the-art semi-supervised video object segmentation methods rely
on a pixel-accurate mask of a target object provided for the first frame of a
video. However, obtaining a detailed segmentation mask is expensive and
time-consuming. In this work we explore an alternative way of identifying a
target object, na... | computer science |
31,634 | Monocular Depth Estimation by Learning from Heterogeneous Datasets | cs.CV | Depth estimation provides essential information to perform autonomous driving
and driver assistance. Especially, Monocular Depth Estimation is interesting
from a practical point of view, since using a single camera is cheaper than
many other options and avoids the need for continuous calibration strategies as
required ... | computer science |
31,635 | Eigendecomposition-free Training of Deep Networks with Zero
Eigenvalue-based Losses | cs.CV | Many classical Computer Vision problems, such as essential matrix computation
and pose estimation from 3D to 2D correspondences, can be solved by finding the
eigenvector corresponding to the smallest, or zero, eigenvalue of a matrix
representing a linear system. Incorporating this in deep learning frameworks
would allo... | computer science |
31,636 | Probabilistic Video Generation using Holistic Attribute Control | cs.CV | Videos express highly structured spatio-temporal patterns of visual data. A
video can be thought of as being governed by two factors: (i) temporally
invariant (e.g., person identity), or slowly varying (e.g., activity),
attribute-induced appearance, encoding the persistent content of each frame,
and (ii) an inter-frame... | computer science |
31,637 | T-RECS: Training for Rate-Invariant Embeddings by Controlling Speed for
Action Recognition | cs.CV | An action should remain identifiable when modifying its speed: consider the
contrast between an expert chef and a novice chef each chopping an onion. Here,
we expect the novice chef to have a relatively measured and slow approach to
chopping when compared to the expert. In general, the speed at which actions
are perfor... | computer science |
31,638 | A Unified Framework for Multi-View Multi-Class Object Pose Estimation | cs.CV | One core challenge in object pose estimation is to ensure accurate and robust
performance for large numbers of diverse foreground objects amidst complex
background clutter. In this work, we present a scalable framework for
accurately inferring six Degree-of-Freedom (6-DoF) pose for a large number of
object classes from... | computer science |
31,639 | Fisher Pruning of Deep Nets for Facial Trait Classification | cs.CV | Although deep nets have resulted in high accuracies for various visual tasks,
their computational and space requirements are prohibitively high for inclusion
on devices without high-end GPUs. In this paper, we introduce a neuron/filter
level pruning framework based on Fisher's LDA which leads to high accuracies
for a w... | computer science |
31,640 | Deep Pose Consensus Networks | cs.CV | In this paper, we address the problem of estimating a 3D human pose from a
single image, which is important but difficult to solve due to many reasons,
such as self-occlusions, wild appearance changes, and inherent ambiguities of
3D estimation from a 2D cue. These difficulties make the problem ill-posed,
which have bec... | computer science |
31,641 | Single-Shot Bidirectional Pyramid Networks for High-Quality Object
Detection | cs.CV | Recent years have witnessed many exciting achievements for object detection
using deep learning techniques. Despite achieving significant progresses, most
existing detectors are designed to detect objects with relatively low-quality
prediction of locations, i.e., often trained with the threshold of Intersection
over Un... | computer science |
31,642 | PersonLab: Person Pose Estimation and Instance Segmentation with a
Bottom-Up, Part-Based, Geometric Embedding Model | cs.CV | We present a box-free bottom-up approach for the tasks of pose estimation and
instance segmentation of people in multi-person images using an efficient
single-shot model. The proposed PersonLab model tackles both semantic-level
reasoning and object-part associations using part-based modeling. Our model
employs a convol... | computer science |
31,643 | Unsupervised Adversarial Learning of 3D Human Pose from 2D Joint
Locations | cs.CV | The task of three-dimensional (3D) human pose estimation from a single image
can be divided into two parts: (1) Two-dimensional (2D) human joint detection
from the image and (2) estimating a 3D pose from the 2D joints. Herein, we
focus on the second part, i.e., a 3D pose estimation from 2D joint locations.
The problem ... | computer science |
31,644 | Show, Tell and Discriminate: Image Captioning by Self-retrieval with
Partially Labeled Data | cs.CV | The aim of image captioning is to generate similar captions by machine as
human do to describe image contents. Despite many efforts, generating
discriminative captions for images remains non-trivial. Most traditional
approaches imitate the language structure patterns, thus tend to fall into a
stereotype of replicating ... | computer science |
31,645 | Learning to Detect and Track Visible and Occluded Body Joints in a
Virtual World | cs.CV | Multi-People Tracking in an open-world setting requires a special effort in
precise detection. Moreover, temporal continuity in the detection phase gains
more importance when scene cluttering introduces the challenging problems of
occluded targets. For the purpose, we propose a deep network architecture that
jointly ex... | computer science |
31,646 | Prioritized Multi-View Stereo Depth Map Generation Using Confidence
Prediction | cs.CV | In this work, we propose a novel approach to prioritize the depth map
computation of multi-view stereo (MVS) to obtain compact 3D point clouds of
high quality and completeness at low computational cost. Our prioritization
approach operates before the MVS algorithm is executed and consists of two
steps. In the first ste... | computer science |
31,647 | Dichromatic Gray Pixel for Camera-agnostic Color Constancy | cs.CV | We propose a novel statistical color constancy method, especially suitable
for the Camera-agnostic Color Constancy, i.e. the scenario where nothing is
known a priori about the capturing devices. The method, called Dichromatic Gray
Pixel, or DGP, relies on a novel gray pixel detection algorithm derived using
the Dichrom... | computer science |
31,648 | Found a good match: should I keep searching? - Accuracy and Performance
in Iris Matching Using 1-to-First Search | cs.CV | Iris recognition is used in many applications around the world, with
enrollment sizes as large as over one billion persons in India's Aadhaar
program. Large enrollment sizes can require special optimizations in order to
achieve fast database searches. One such optimization that has been used in
some operational scenari... | computer science |
31,649 | PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction | cs.CV | We introduce a novel RGB-D patch descriptor designed for detecting coplanar
surfaces in SLAM reconstruction. The core of our method is a deep convolutional
neural net that takes in RGB, depth, and normal information of a planar patch
in an image and outputs a descriptor that can be used to find coplanar patches
from ot... | computer science |
31,650 | A Smoke Removal Method for Laparoscopic Images | cs.CV | In laparoscopic surgery, image quality can be severely degraded by surgical
smoke, which not only introduces error for the image processing (used in image
guided surgery), but also reduces the visibility of the surgeons. In this
paper, we propose to enhance the laparoscopic images by decomposing them into
unwanted smok... | computer science |
31,651 | Group Sparsity Residual with Non-Local Samples for Image Denoising | cs.CV | Inspired by group-based sparse coding, recently proposed group sparsity
residual (GSR) scheme has demonstrated superior performance in image
processing. However, one challenge in GSR is to estimate the residual by using
a proper reference of the group-based sparse coding (GSC), which is desired to
be as close to the tr... | computer science |
31,652 | Buried object detection from B-scan ground penetrating radar data using
Faster-RCNN | cs.CV | In this paper, we adapt the Faster-RCNN framework for the detection of
underground buried objects (i.e. hyperbola reflections) in B-scan ground
penetrating radar (GPR) images. Due to the lack of real data for training, we
propose to incorporate more simulated radargrams generated from different
configurations using the... | computer science |
31,653 | Guided Image Inpainting: Replacing an Image Region by Pulling Content
from Another Image | cs.CV | Deep generative models have shown success in automatically synthesizing
missing image regions using surrounding context. However, users cannot directly
decide what content to synthesize with such approaches. We propose an
end-to-end network for image inpainting that uses a different image to guide
the synthesis of new ... | computer science |
31,654 | A Comprehensive Analysis of Deep Regression | cs.CV | Deep learning revolutionized data science, and recently, its popularity has
grown exponentially, as did the amount of papers employing deep networks.
Vision tasks such as human pose estimation did not escape this methodological
change. The large number of deep architectures lead to a plethora of methods
that are evalua... | computer science |
31,655 | Clustering-driven Deep Embedding with Pairwise Constraints | cs.CV | Recently, there has been increasing interest to leverage the competence of
neural networks to analyze data. In particular, new clustering methods that
employ deep embeddings have been presented. In this paper, we depart from
centroid-based models and suggest a new framework, called Clustering-driven
deep embedding with... | computer science |
31,656 | Branched Generative Adversarial Networks for Multi-Scale Image Manifold
Learning | cs.CV | We introduce BranchGAN, a novel training method that enables unconditioned
generative adversarial networks (GANs) to learn image manifolds at multiple
scales. What is unique about BranchGAN is that it is trained in multiple
branches, progressively covering both the breadth and depth of the network, as
resolutions of th... | computer science |
31,657 | KonIQ-10k: Towards an ecologically valid and large-scale IQA database | cs.CV | The main challenge in applying state-of-the-art deep learning methods to
predict image quality in-the-wild is the relatively small size of existing
quality scored datasets. The reason for the lack of larger datasets is the
massive resources required in generating diverse and publishable content. We
present a new system... | computer science |
31,658 | Generalized Scene Reconstruction | cs.CV | A new passive approach called Generalized Scene Reconstruction (GSR) enables
"generalized scenes" to be effectively reconstructed. Generalized scenes are
defined to be "boundless" spaces that include non-Lambertian, partially
transmissive, textureless and finely-structured matter. A new data structure
called a plenopti... | computer science |
31,659 | Hierarchical Reinforcement Learning with the MAXQ Value Function
Decomposition | cs.LG | This paper presents the MAXQ approach to hierarchical reinforcement learning
based on decomposing the target Markov decision process (MDP) into a hierarchy
of smaller MDPs and decomposing the value function of the target MDP into an
additive combination of the value functions of the smaller MDPs. The paper
defines the ... | computer science |
31,660 | State Abstraction in MAXQ Hierarchical Reinforcement Learning | cs.LG | Many researchers have explored methods for hierarchical reinforcement
learning (RL) with temporal abstractions, in which abstract actions are defined
that can perform many primitive actions before terminating. However, little is
known about learning with state abstractions, in which aspects of the state
space are ignor... | computer science |
31,661 | Multiplicative Algorithm for Orthgonal Groups and Independent Component
Analysis | cs.LG | The multiplicative Newton-like method developed by the author et al. is
extended to the situation where the dynamics is restricted to the orthogonal
group. A general framework is constructed without specifying the cost function.
Though the restriction to the orthogonal groups makes the problem somewhat
complicated, an ... | computer science |
31,662 | Multiplicative Nonholonomic/Newton -like Algorithm | cs.LG | We construct new algorithms from scratch, which use the fourth order cumulant
of stochastic variables for the cost function. The multiplicative updating rule
here constructed is natural from the homogeneous nature of the Lie group and
has numerous merits for the rigorous treatment of the dynamics. As one
consequence, t... | computer science |
31,663 | Complexity analysis for algorithmically simple strings | cs.LG | Given a reference computer, Kolmogorov complexity is a well defined function
on all binary strings. In the standard approach, however, only the asymptotic
properties of such functions are considered because they do not depend on the
reference computer. We argue that this approach can be more useful if it is
refined to ... | computer science |
31,664 | Robust Classification for Imprecise Environments | cs.LG | In real-world environments it usually is difficult to specify target
operating conditions precisely, for example, target misclassification costs.
This uncertainty makes building robust classification systems problematic. We
show that it is possible to build a hybrid classifier that will perform at
least as well as the ... | computer science |
31,665 | Top-down induction of clustering trees | cs.LG | An approach to clustering is presented that adapts the basic top-down
induction of decision trees method towards clustering. To this aim, it employs
the principles of instance based learning. The resulting methodology is
implemented in the TIC (Top down Induction of Clustering trees) system for
first order clustering. ... | computer science |
31,666 | Scaling Up Inductive Logic Programming by Learning from Interpretations | cs.LG | When comparing inductive logic programming (ILP) and attribute-value learning
techniques, there is a trade-off between expressive power and efficiency.
Inductive logic programming techniques are typically more expressive but also
less efficient. Therefore, the data sets handled by current inductive logic
programming sy... | computer science |
31,667 | Learning Policies with External Memory | cs.LG | In order for an agent to perform well in partially observable domains, it is
usually necessary for actions to depend on the history of observations. In this
paper, we explore a {\it stigmergic} approach, in which the agent's actions
include the ability to set and clear bits in an external memory, and the
external memor... | computer science |
31,668 | Efficient algorithms for decision tree cross-validation | cs.LG | Cross-validation is a useful and generally applicable technique often
employed in machine learning, including decision tree induction. An important
disadvantage of straightforward implementation of the technique is its
computational overhead. In this paper we show that, for decision trees, the
computational overhead of... | computer science |
31,669 | Evaluation of the Performance of the Markov Blanket Bayesian Classifier
Algorithm | cs.LG | The Markov Blanket Bayesian Classifier is a recently-proposed algorithm for
construction of probabilistic classifiers. This paper presents an empirical
comparison of the MBBC algorithm with three other Bayesian classifiers: Naive
Bayes, Tree-Augmented Naive Bayes and a general Bayesian network. All of these
are impleme... | computer science |
31,670 | Approximating Incomplete Kernel Matrices by the em Algorithm | cs.LG | In biological data, it is often the case that observed data are available
only for a subset of samples. When a kernel matrix is derived from such data,
we have to leave the entries for unavailable samples as missing. In this paper,
we make use of a parametric model of kernel matrices, and estimate missing
entries by fi... | computer science |
31,671 | Reliable and Efficient Inference of Bayesian Networks from Sparse Data
by Statistical Learning Theory | cs.LG | To learn (statistical) dependencies among random variables requires
exponentially large sample size in the number of observed random variables if
any arbitrary joint probability distribution can occur.
We consider the case that sparse data strongly suggest that the probabilities
can be described by a simple Bayesian ... | computer science |
31,672 | Toward Attribute Efficient Learning Algorithms | cs.LG | We make progress on two important problems regarding attribute efficient
learnability.
First, we give an algorithm for learning decision lists of length $k$ over
$n$ variables using $2^{\tilde{O}(k^{1/3})} \log n$ examples and time
$n^{\tilde{O}(k^{1/3})}$. This is the first algorithm for learning decision
lists that... | computer science |
31,673 | Improving spam filtering by combining Naive Bayes with simple k-nearest
neighbor searches | cs.LG | Using naive Bayes for email classification has become very popular within the
last few months. They are quite easy to implement and very efficient. In this
paper we want to present empirical results of email classification using a
combination of naive Bayes and k-nearest neighbor searches. Using this
technique we show ... | computer science |
31,674 | About Unitary Rating Score Constructing | cs.LG | It is offered to pool test points of different subjects and different aspects
of the same subject together in order to get the unitary rating score, by the
way of nonlinear transformation of indicator points in accordance with Zipf's
distribution. It is proposed to use the well-studied distribution of
Intellectuality Q... | computer science |
31,675 | Mining Heterogeneous Multivariate Time-Series for Learning Meaningful
Patterns: Application to Home Health Telecare | cs.LG | For the last years, time-series mining has become a challenging issue for
researchers. An important application lies in most monitoring purposes, which
require analyzing large sets of time-series for learning usual patterns. Any
deviation from this learned profile is then considered as an unexpected
situation. Moreover... | computer science |
31,676 | Stability Analysis for Regularized Least Squares Regression | cs.LG | We discuss stability for a class of learning algorithms with respect to noisy
labels. The algorithms we consider are for regression, and they involve the
minimization of regularized risk functionals, such as L(f) := 1/N sum_i
(f(x_i)-y_i)^2+ lambda ||f||_H^2. We shall call the algorithm `stable' if, when
y_i is a noisy... | computer science |
31,677 | Probabilistic and Team PFIN-type Learning: General Properties | cs.LG | We consider the probability hierarchy for Popperian FINite learning and study
the general properties of this hierarchy. We prove that the probability
hierarchy is decidable, i.e. there exists an algorithm that receives p_1 and
p_2 and answers whether PFIN-type learning with the probability of success p_1
is equivalent ... | computer science |
31,678 | Non-asymptotic calibration and resolution | cs.LG | We analyze a new algorithm for probability forecasting of binary observations
on the basis of the available data, without making any assumptions about the
way the observations are generated. The algorithm is shown to be well
calibrated and to have good resolution for long enough sequences of
observations and for a suit... | computer science |
31,679 | Defensive forecasting for linear protocols | cs.LG | We consider a general class of forecasting protocols, called "linear
protocols", and discuss several important special cases, including multi-class
forecasting. Forecasting is formalized as a game between three players:
Reality, whose role is to generate observations; Forecaster, whose goal is to
predict the observatio... | computer science |
31,680 | About one 3-parameter Model of Testing | cs.LG | This article offers a 3-parameter model of testing, with 1) the difference
between the ability level of the examinee and item difficulty; 2) the examinee
discrimination and 3) the item discrimination as model parameters. | computer science |
31,681 | On the Job Training | cs.LG | We propose a new framework for building and evaluating machine learning
algorithms. We argue that many real-world problems require an agent which must
quickly learn to respond to demands, yet can continue to perform and respond to
new training throughout its useful life. We give a framework for how such
agents can be b... | computer science |
31,682 | Multiresolution Kernels | cs.LG | We present in this work a new methodology to design kernels on data which is
structured with smaller components, such as text, images or sequences. This
methodology is a template procedure which can be applied on most kernels on
measures and takes advantage of a more detailed "bag of components"
representation of the o... | computer science |
31,683 | Defensive Universal Learning with Experts | cs.LG | This paper shows how universal learning can be achieved with expert advice.
To this aim, we specify an experts algorithm with the following
characteristics: (a) it uses only feedback from the actions actually chosen
(bandit setup), (b) it can be applied with countably infinite expert classes,
and (c) it copes with loss... | computer science |
31,684 | FPL Analysis for Adaptive Bandits | cs.LG | A main problem of "Follow the Perturbed Leader" strategies for online
decision problems is that regret bounds are typically proven against oblivious
adversary. In partial observation cases, it was not clear how to obtain
performance guarantees against adaptive adversary, without worsening the
bounds. We propose a conce... | computer science |
31,685 | Learning Optimal Augmented Bayes Networks | cs.LG | Naive Bayes is a simple Bayesian classifier with strong independence
assumptions among the attributes. This classifier, desipte its strong
independence assumptions, often performs well in practice. It is believed that
relaxing the independence assumptions of a naive Bayes classifier may improve
the classification accur... | computer science |
31,686 | Learning Unions of $ω(1)$-Dimensional Rectangles | cs.LG | We consider the problem of learning unions of rectangles over the domain
$[b]^n$, in the uniform distribution membership query learning setting, where
both b and n are "large". We obtain poly$(n, \log b)$-time algorithms for the
following classes:
- poly$(n \log b)$-way Majority of $O(\frac{\log(n \log b)} {\log \log... | computer science |
31,687 | On-line regression competitive with reproducing kernel Hilbert spaces | cs.LG | We consider the problem of on-line prediction of real-valued labels, assumed
bounded in absolute value by a known constant, of new objects from known
labeled objects. The prediction algorithm's performance is measured by the
squared deviation of the predictions from the actual labels. No stochastic
assumptions are made... | computer science |
31,688 | Bounds on Query Convergence | cs.LG | The problem of finding an optimum using noisy evaluations of a smooth cost
function arises in many contexts, including economics, business, medicine,
experiment design, and foraging theory. We derive an asymptotic bound E[ (x_t -
x*)^2 ] >= O(1/sqrt(t)) on the rate of convergence of a sequence (x_0, x_1,
>...) generate... | computer science |
31,689 | Preference Learning in Terminology Extraction: A ROC-based approach | cs.LG | A key data preparation step in Text Mining, Term Extraction selects the
terms, or collocation of words, attached to specific concepts. In this paper,
the task of extracting relevant collocations is achieved through a supervised
learning algorithm, exploiting a few collocations manually labelled as
relevant/irrelevant. ... | computer science |
31,690 | Competing with wild prediction rules | cs.LG | We consider the problem of on-line prediction competitive with a benchmark
class of continuous but highly irregular prediction rules. It is known that if
the benchmark class is a reproducing kernel Hilbert space, there exists a
prediction algorithm whose average loss over the first N examples does not
exceed the averag... | computer science |
31,691 | Genetic Programming, Validation Sets, and Parsimony Pressure | cs.LG | Fitness functions based on test cases are very common in Genetic Programming
(GP). This process can be assimilated to a learning task, with the inference of
models from a limited number of samples. This paper is an investigation on two
methods to improve generalization in GP-based learning: 1) the selection of the
best... | computer science |
31,692 | Processing of Test Matrices with Guessing Correction | cs.LG | It is suggested to insert into test matrix 1s for correct responses, 0s for
response refusals, and negative corrective elements for incorrect responses.
With the classical test theory approach test scores of examinees and items are
calculated traditionally as sums of matrix elements, organized in rows and
columns. Corr... | computer science |
31,693 | Learning rational stochastic languages | cs.LG | Given a finite set of words w1,...,wn independently drawn according to a
fixed unknown distribution law P called a stochastic language, an usual goal in
Grammatical Inference is to infer an estimate of P in some class of
probabilistic models, such as Probabilistic Automata (PA). Here, we study the
class of rational sto... | computer science |
31,694 | General Discounting versus Average Reward | cs.LG | Consider an agent interacting with an environment in cycles. In every
interaction cycle the agent is rewarded for its performance. We compare the
average reward U from cycle 1 to m (average value) with the future discounted
reward V from cycle k to infinity (discounted value). We consider essentially
arbitrary (non-geo... | computer science |
31,695 | On Sequence Prediction for Arbitrary Measures | cs.LG | Suppose we are given two probability measures on the set of one-way infinite
finite-alphabet sequences and consider the question when one of the measures
predicts the other, that is, when conditional probabilities converge (in a
certain sense) when one of the measures is chosen to generate the sequence.
This question m... | computer science |
31,696 | Predictions as statements and decisions | cs.LG | Prediction is a complex notion, and different predictors (such as people,
computer programs, and probabilistic theories) can pursue very different goals.
In this paper I will review some popular kinds of prediction and argue that the
theory of competitive on-line learning can benefit from the kinds of prediction
that a... | computer science |
31,697 | PAC Classification based on PAC Estimates of Label Class Distributions | cs.LG | A standard approach in pattern classification is to estimate the
distributions of the label classes, and then to apply the Bayes classifier to
the estimates of the distributions in order to classify unlabeled examples. As
one might expect, the better our estimates of the label class distributions,
the better the result... | computer science |
31,698 | Competing with stationary prediction strategies | cs.LG | In this paper we introduce the class of stationary prediction strategies and
construct a prediction algorithm that asymptotically performs as well as the
best continuous stationary strategy. We make mild compactness assumptions but
no stochastic assumptions about the environment. In particular, no assumption
of station... | computer science |
31,699 | Using Pseudo-Stochastic Rational Languages in Probabilistic Grammatical
Inference | cs.LG | In probabilistic grammatical inference, a usual goal is to infer a good
approximation of an unknown distribution P called a stochastic language. The
estimate of P stands in some class of probabilistic models such as
probabilistic automata (PA). In this paper, we focus on probabilistic models
based on multiplicity autom... | computer science |
31,700 | Logical settings for concept learning from incomplete examples in First
Order Logic | cs.LG | We investigate here concept learning from incomplete examples. Our first
purpose is to discuss to what extent logical learning settings have to be
modified in order to cope with data incompleteness. More precisely we are
interested in extending the learning from interpretations setting introduced by
L. De Raedt that ex... | computer science |
31,701 | A Theory of Probabilistic Boosting, Decision Trees and Matryoshki | cs.LG | We present a theory of boosting probabilistic classifiers. We place ourselves
in the situation of a user who only provides a stopping parameter and a
probabilistic weak learner/classifier and compare three types of boosting
algorithms: probabilistic Adaboost, decision tree, and tree of trees of ... of
trees, which we c... | computer science |
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