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4,700 | Learning Bayesian Networks from Incomplete Databases | cs.AI | Bayesian approaches to learn the graphical structure of Bayesian Belief
Networks (BBNs) from databases share the assumption that the database is
complete, that is, no entry is reported as unknown. Attempts to relax this
assumption involve the use of expensive iterative methods to discriminate among
different structures... | computer science |
4,701 | A Characterization of the Dirichlet Distribution with Application to
Learning Bayesian Networks | cs.AI | We provide a new characterization of the Dirichlet distribution. This
characterization implies that under assumptions made by several previous
authors for learning belief networks, a Dirichlet prior on the parameters is
inevitable. | computer science |
4,702 | Arriving on time: estimating travel time distributions on large-scale
road networks | cs.LG | Most optimal routing problems focus on minimizing travel time or distance
traveled. Oftentimes, a more useful objective is to maximize the probability of
on-time arrival, which requires statistical distributions of travel times,
rather than just mean values. We propose a method to estimate travel time
distributions on ... | computer science |
4,703 | High Level Pattern Classification via Tourist Walks in Networks | cs.AI | Complex networks refer to large-scale graphs with nontrivial connection
patterns. The salient and interesting features that the complex network study
offer in comparison to graph theory are the emphasis on the dynamical
properties of the networks and the ability of inherently uncovering pattern
formation of the vertice... | computer science |
4,704 | Stochastic gradient descent algorithms for strongly convex functions at
O(1/T) convergence rates | cs.LG | With a weighting scheme proportional to t, a traditional stochastic gradient
descent (SGD) algorithm achieves a high probability convergence rate of
O({\kappa}/T) for strongly convex functions, instead of O({\kappa} ln(T)/T). We
also prove that an accelerated SGD algorithm also achieves a rate of
O({\kappa}/T). | computer science |
4,705 | A Data Mining Approach to Solve the Goal Scoring Problem | cs.AI | In soccer, scoring goals is a fundamental objective which depends on many
conditions and constraints. Considering the RoboCup soccer 2D-simulator, this
paper presents a data mining-based decision system to identify the best time
and direction to kick the ball towards the goal to maximize the overall chances
of scoring ... | computer science |
4,706 | Beyond One-Step-Ahead Forecasting: Evaluation of Alternative
Multi-Step-Ahead Forecasting Models for Crude Oil Prices | cs.LG | An accurate prediction of crude oil prices over long future horizons is
challenging and of great interest to governments, enterprises, and investors.
This paper proposes a revised hybrid model built upon empirical mode
decomposition (EMD) based on the feed-forward neural network (FNN) modeling
framework incorporating t... | computer science |
4,707 | Analogical Dissimilarity: Definition, Algorithms and Two Experiments in
Machine Learning | cs.LG | This paper defines the notion of analogical dissimilarity between four
objects, with a special focus on objects structured as sequences. Firstly, it
studies the case where the four objects have a null analogical dissimilarity,
i.e. are in analogical proportion. Secondly, when one of these objects is
unknown, it gives a... | computer science |
4,708 | A Rigorously Bayesian Beam Model and an Adaptive Full Scan Model for
Range Finders in Dynamic Environments | cs.AI | This paper proposes and experimentally validates a Bayesian network model of
a range finder adapted to dynamic environments. All modeling assumptions are
rigorously explained, and all model parameters have a physical interpretation.
This approach results in a transparent and intuitive model. With respect to the
state o... | computer science |
4,709 | Non-Deterministic Policies in Markovian Decision Processes | cs.AI | Markovian processes have long been used to model stochastic environments.
Reinforcement learning has emerged as a framework to solve sequential planning
and decision-making problems in such environments. In recent years, attempts
were made to apply methods from reinforcement learning to construct decision
support syste... | computer science |
4,710 | Combining Evaluation Metrics via the Unanimous Improvement Ratio and its
Application to Clustering Tasks | cs.AI | Many Artificial Intelligence tasks cannot be evaluated with a single quality
criterion and some sort of weighted combination is needed to provide system
rankings. A problem of weighted combination measures is that slight changes in
the relative weights may produce substantial changes in the system rankings.
This paper ... | computer science |
4,711 | Parameterized Complexity Results for Exact Bayesian Network Structure
Learning | cs.AI | Bayesian network structure learning is the notoriously difficult problem of
discovering a Bayesian network that optimally represents a given set of
training data. In this paper we study the computational worst-case complexity
of exact Bayesian network structure learning under graph theoretic restrictions
on the (direct... | computer science |
4,712 | Safe Exploration of State and Action Spaces in Reinforcement Learning | cs.LG | In this paper, we consider the important problem of safe exploration in
reinforcement learning. While reinforcement learning is well-suited to domains
with complex transition dynamics and high-dimensional state-action spaces, an
additional challenge is posed by the need for safe and efficient exploration.
Traditional e... | computer science |
4,713 | Near Optimal Bayesian Active Learning for Decision Making | cs.LG | How should we gather information to make effective decisions? We address
Bayesian active learning and experimental design problems, where we
sequentially select tests to reduce uncertainty about a set of hypotheses.
Instead of minimizing uncertainty per se, we consider a set of overlapping
decision regions of these hyp... | computer science |
4,714 | Incremental Learning of Event Definitions with Inductive Logic
Programming | cs.LG | Event recognition systems rely on properly engineered knowledge bases of
event definitions to infer occurrences of events in time. The manual
development of such knowledge is a tedious and error-prone task, thus
event-based applications may benefit from automated knowledge construction
techniques, such as Inductive Log... | computer science |
4,715 | Algorithms for multi-armed bandit problems | cs.AI | Although many algorithms for the multi-armed bandit problem are
well-understood theoretically, empirical confirmation of their effectiveness is
generally scarce. This paper presents a thorough empirical study of the most
popular multi-armed bandit algorithms. Three important observations can be made
from our results. F... | computer science |
4,716 | Inductive Logic Boosting | cs.LG | Recent years have seen a surge of interest in Probabilistic Logic Programming
(PLP) and Statistical Relational Learning (SRL) models that combine logic with
probabilities. Structure learning of these systems is an intersection area of
Inductive Logic Programming (ILP) and statistical learning (SL). However, ILP
cannot ... | computer science |
4,717 | Updating Formulas and Algorithms for Computing Entropy and Gini Index
from Time-Changing Data Streams | cs.AI | Despite growing interest in data stream mining the most successful
incremental learners, such as VFDT, still use periodic recomputation to update
attribute information gains and Gini indices. This note provides simple
incremental formulas and algorithms for computing entropy and Gini index from
time-changing data strea... | computer science |
4,718 | Exchangeable Variable Models | cs.LG | A sequence of random variables is exchangeable if its joint distribution is
invariant under variable permutations. We introduce exchangeable variable
models (EVMs) as a novel class of probabilistic models whose basic building
blocks are partially exchangeable sequences, a generalization of exchangeable
sequences. We pr... | computer science |
4,719 | Adaptive Monte Carlo via Bandit Allocation | cs.AI | We consider the problem of sequentially choosing between a set of unbiased
Monte Carlo estimators to minimize the mean-squared-error (MSE) of a final
combined estimate. By reducing this task to a stochastic multi-armed bandit
problem, we show that well developed allocation strategies can be used to
achieve an MSE that ... | computer science |
4,720 | Off-Policy Shaping Ensembles in Reinforcement Learning | cs.AI | Recent advances of gradient temporal-difference methods allow to learn
off-policy multiple value functions in parallel with- out sacrificing
convergence guarantees or computational efficiency. This opens up new
possibilities for sound ensemble techniques in reinforcement learning. In this
work we propose learning an en... | computer science |
4,721 | Quantum Annealing for Clustering | cs.AI | This paper studies quantum annealing (QA) for clustering, which can be seen
as an extension of simulated annealing (SA). We derive a QA algorithm for
clustering and propose an annealing schedule, which is crucial in practice.
Experiments show the proposed QA algorithm finds better clustering assignments
than SA. Furthe... | computer science |
4,722 | Efficient Clustering with Limited Distance Information | cs.LG | Given a point set S and an unknown metric d on S, we study the problem of
efficiently partitioning S into k clusters while querying few distances between
the points. In our model we assume that we have access to one versus all
queries that given a point s 2 S return the distances between s and all other
points. We show... | computer science |
4,723 | Exponentiated Gradient Exploration for Active Learning | cs.LG | Active learning strategies respond to the costly labelling task in a
supervised classification by selecting the most useful unlabelled examples in
training a predictive model. Many conventional active learning algorithms focus
on refining the decision boundary, rather than exploring new regions that can
be more informa... | computer science |
4,724 | A two-stage architecture for stock price forecasting by combining SOM
and fuzzy-SVM | cs.AI | This paper proposed a model to predict the stock price based on combining
Self-Organizing Map (SOM) and fuzzy-Support Vector Machines (f-SVM). Extraction
of fuzzy rules from raw data based on the combining of statistical machine
learning models is base of this proposed approach. In the proposed model, SOM
is used as a ... | computer science |
4,725 | Improving the Interpretability of Support Vector Machines-based Fuzzy
Rules | cs.LG | Support vector machines (SVMs) and fuzzy rule systems are functionally
equivalent under some conditions. Therefore, the learning algorithms developed
in the field of support vector machines can be used to adapt the parameters of
fuzzy systems. Extracting fuzzy models from support vector machines has the
inherent advant... | computer science |
4,726 | Injury risk prediction for traffic accidents in Porto Alegre/RS, Brazil | cs.LG | This study describes the experimental application of Machine Learning
techniques to build prediction models that can assess the injury risk
associated with traffic accidents. This work uses an freely available data set
of traffic accident records that took place in the city of Porto Alegre/RS
(Brazil) during the year o... | computer science |
4,727 | Random Coordinate Descent Methods for Minimizing Decomposable Submodular
Functions | cs.LG | Submodular function minimization is a fundamental optimization problem that
arises in several applications in machine learning and computer vision. The
problem is known to be solvable in polynomial time, but general purpose
algorithms have high running times and are unsuitable for large-scale problems.
Recent work have... | computer science |
4,728 | Deep Learning for Multi-label Classification | cs.LG | In multi-label classification, the main focus has been to develop ways of
learning the underlying dependencies between labels, and to take advantage of
this at classification time. Developing better feature-space representations
has been predominantly employed to reduce complexity, e.g., by eliminating
non-helpful feat... | computer science |
4,729 | Sequential Relevance Maximization with Binary Feedback | cs.LG | Motivated by online settings where users can provide explicit feedback about
the relevance of products that are sequentially presented to them, we look at
the recommendation process as a problem of dynamically optimizing this
relevance feedback. Such an algorithm optimizes the fine tradeoff between
presenting the produ... | computer science |
4,730 | Efficient Machine Learning for Big Data: A Review | cs.LG | With the emerging technologies and all associated devices, it is predicted
that massive amount of data will be created in the next few years, in fact, as
much as 90% of current data were created in the last couple of years,a trend
that will continue for the foreseeable future. Sustainable computing studies
the process ... | computer science |
4,731 | A Note on Information-Directed Sampling and Thompson Sampling | cs.LG | This note introduce three Bayesian style Multi-armed bandit algorithms:
Information-directed sampling, Thompson Sampling and Generalized Thompson
Sampling. The goal is to give an intuitive explanation for these three
algorithms and their regret bounds, and provide some derivations that are
omitted in the original paper... | computer science |
4,732 | Towards Shockingly Easy Structured Classification: A Search-based
Probabilistic Online Learning Framework | cs.LG | There are two major approaches for structured classification. One is the
probabilistic gradient-based methods such as conditional random fields (CRF),
which has high accuracy but with drawbacks: slow training, and no support of
search-based optimization (which is important in many cases). The other one is
the search-ba... | computer science |
4,733 | Interaction Networks for Learning about Objects, Relations and Physics | cs.AI | Reasoning about objects, relations, and physics is central to human
intelligence, and a key goal of artificial intelligence. Here we introduce the
interaction network, a model which can reason about how objects in complex
systems interact, supporting dynamical predictions, as well as inferences about
the abstract prope... | computer science |
4,734 | A Compositional Object-Based Approach to Learning Physical Dynamics | cs.AI | We present the Neural Physics Engine (NPE), a framework for learning
simulators of intuitive physics that naturally generalize across variable
object count and different scene configurations. We propose a factorization of
a physical scene into composable object-based representations and a neural
network architecture wh... | computer science |
4,735 | Extend natural neighbor: a novel classification method with
self-adaptive neighborhood parameters in different stages | cs.AI | Various kinds of k-nearest neighbor (KNN) based classification methods are
the bases of many well-established and high-performance pattern-recognition
techniques, but both of them are vulnerable to their parameter choice.
Essentially, the challenge is to detect the neighborhood of various data sets,
while utterly ignor... | computer science |
4,736 | Online Reinforcement Learning for Real-Time Exploration in Continuous
State and Action Markov Decision Processes | cs.AI | This paper presents a new method to learn online policies in continuous
state, continuous action, model-free Markov decision processes, with two
properties that are crucial for practical applications. First, the policies are
implementable with a very low computational cost: once the policy is computed,
the action corre... | computer science |
4,737 | Anomaly Detection Using the Knowledge-based Temporal Abstraction Method | cs.LG | The rapid growth in stored time-oriented data necessitates the development of
new methods for handling, processing, and interpreting large amounts of
temporal data. One important example of such processing is detecting anomalies
in time-oriented data. The Knowledge-Based Temporal Abstraction method was
previously propo... | computer science |
4,738 | Separation of Concerns in Reinforcement Learning | cs.LG | In this paper, we propose a framework for solving a single-agent task by
using multiple agents, each focusing on different aspects of the task. This
approach has two main advantages: 1) it allows for training specialized agents
on different parts of the task, and 2) it provides a new way to transfer
knowledge, by trans... | computer science |
4,739 | A Survey of Inductive Biases for Factorial Representation-Learning | cs.LG | With the resurgence of interest in neural networks, representation learning
has re-emerged as a central focus in artificial intelligence. Representation
learning refers to the discovery of useful encodings of data that make
domain-relevant information explicit. Factorial representations identify
underlying independent ... | computer science |
4,740 | Defensive Player Classification in the National Basketball Association | cs.LG | The National Basketball Association(NBA) has expanded their data gathering
and have heavily invested in new technologies to gather advanced performance
metrics on players. This expanded data set allows analysts to use unique
performance metrics in models to estimate and classify player performance.
Instead of grouping ... | computer science |
4,741 | Self-Correcting Models for Model-Based Reinforcement Learning | cs.LG | When an agent cannot represent a perfectly accurate model of its
environment's dynamics, model-based reinforcement learning (MBRL) can fail
catastrophically. Planning involves composing the predictions of the model;
when flawed predictions are composed, even minor errors can compound and render
the model useless for pl... | computer science |
4,742 | Parallelized Tensor Train Learning of Polynomial Classifiers | cs.LG | In pattern classification, polynomial classifiers are well-studied methods as
they are capable of generating complex decision surfaces. Unfortunately, the
use of multivariate polynomials is limited to kernels as in support vector
machines, because polynomials quickly become impractical for high-dimensional
problems. In... | computer science |
4,743 | A Base Camp for Scaling AI | cs.AI | Modern statistical machine learning (SML) methods share a major limitation
with the early approaches to AI: there is no scalable way to adapt them to new
domains. Human learning solves this in part by leveraging a rich, shared,
updateable world model. Such scalability requires modularity: updating part of
the world mod... | computer science |
4,744 | Preconditioned Temporal Difference Learning | cs.LG | This paper has been withdrawn by the author. This draft is withdrawn for its
poor quality in english, unfortunately produced by the author when he was just
starting his science route. Look at the ICML version instead:
http://icml2008.cs.helsinki.fi/papers/111.pdf | computer science |
4,745 | Building Rules on Top of Ontologies for the Semantic Web with Inductive
Logic Programming | cs.AI | Building rules on top of ontologies is the ultimate goal of the logical layer
of the Semantic Web. To this aim an ad-hoc mark-up language for this layer is
currently under discussion. It is intended to follow the tradition of hybrid
knowledge representation and reasoning systems such as $\mathcal{AL}$-log that
integrat... | computer science |
4,746 | A New Understanding of Prediction Markets Via No-Regret Learning | cs.AI | We explore the striking mathematical connections that exist between market
scoring rules, cost function based prediction markets, and no-regret learning.
We show that any cost function based prediction market can be interpreted as an
algorithm for the commonly studied problem of learning from expert advice by
equating ... | computer science |
4,747 | Learning from Logged Implicit Exploration Data | cs.LG | We provide a sound and consistent foundation for the use of \emph{nonrandom}
exploration data in "contextual bandit" or "partially labeled" settings where
only the value of a chosen action is learned.
The primary challenge in a variety of settings is that the exploration
policy, in which "offline" data is logged, is ... | computer science |
4,748 | A Brief Introduction to Temporality and Causality | cs.LG | Causality is a non-obvious concept that is often considered to be related to
temporality. In this paper we present a number of past and present approaches
to the definition of temporality and causality from philosophical, physical,
and computational points of view. We note that time is an important ingredient
in many r... | computer science |
4,749 | Separate Training for Conditional Random Fields Using Co-occurrence Rate
Factorization | cs.LG | The standard training method of Conditional Random Fields (CRFs) is very slow
for large-scale applications. As an alternative, piecewise training divides the
full graph into pieces, trains them independently, and combines the learned
weights at test time. In this paper, we present \emph{separate} training for
undirecte... | computer science |
4,750 | A Learning Algorithm based on High School Teaching Wisdom | cs.AI | A learning algorithm based on primary school teaching and learning is
presented. The methodology is to continuously evaluate a student and to give
them training on the examples for which they repeatedly fail, until, they can
correctly answer all types of questions. This incremental learning procedure
produces better le... | computer science |
4,751 | POWERPLAY: Training an Increasingly General Problem Solver by
Continually Searching for the Simplest Still Unsolvable Problem | cs.AI | Most of computer science focuses on automatically solving given computational
problems. I focus on automatically inventing or discovering problems in a way
inspired by the playful behavior of animals and humans, to train a more and
more general problem solver from scratch in an unsupervised fashion. Consider
the infini... | computer science |
4,752 | Learning using Local Membership Queries | cs.LG | We introduce a new model of membership query (MQ) learning, where the
learning algorithm is restricted to query points that are \emph{close} to
random examples drawn from the underlying distribution. The learning model is
intermediate between the PAC model (Valiant, 1984) and the PAC+MQ model (where
the queries are all... | computer science |
4,753 | Minimal cost feature selection of data with normal distribution
measurement errors | cs.AI | Minimal cost feature selection is devoted to obtain a trade-off between test
costs and misclassification costs. This issue has been addressed recently on
nominal data. In this paper, we consider numerical data with measurement errors
and study minimal cost feature selection in this model. First, we build a data
model w... | computer science |
4,754 | Distributed Non-Stochastic Experts | cs.LG | We consider the online distributed non-stochastic experts problem, where the
distributed system consists of one coordinator node that is connected to $k$
sites, and the sites are required to communicate with each other via the
coordinator. At each time-step $t$, one of the $k$ site nodes has to pick an
expert from the ... | computer science |
4,755 | Optimally fuzzy temporal memory | cs.AI | Any learner with the ability to predict the future of a structured
time-varying signal must maintain a memory of the recent past. If the signal
has a characteristic timescale relevant to future prediction, the memory can be
a simple shift register---a moving window extending into the past, requiring
storage resources t... | computer science |
4,756 | On the Use of Non-Stationary Policies for Stationary Infinite-Horizon
Markov Decision Processes | cs.LG | We consider infinite-horizon stationary $\gamma$-discounted Markov Decision
Processes, for which it is known that there exists a stationary optimal policy.
Using Value and Policy Iteration with some error $\epsilon$ at each iteration,
it is well-known that one can compute stationary policies that are
$\frac{2\gamma}{(1... | computer science |
4,757 | Learning to Understand by Evolving Theories | cs.LG | In this paper, we describe an approach that enables an autonomous system to
infer the semantics of a command (i.e. a symbol sequence representing an
action) in terms of the relations between changes in the observations and the
action instances. We present a method of how to induce a theory (i.e. a
semantic description)... | computer science |
4,758 | Multi-dimensional Parametric Mincuts for Constrained MAP Inference | cs.LG | In this paper, we propose novel algorithms for inferring the Maximum a
Posteriori (MAP) solution of discrete pairwise random field models under
multiple constraints. We show how this constrained discrete optimization
problem can be formulated as a multi-dimensional parametric mincut problem via
its Lagrangian dual, and... | computer science |
4,759 | Spontaneous Analogy by Piggybacking on a Perceptual System | cs.AI | Most computational models of analogy assume they are given a delineated
source domain and often a specified target domain. These systems do not address
how analogs can be isolated from large domains and spontaneously retrieved from
long-term memory, a process we call spontaneous analogy. We present a system
that repres... | computer science |
4,760 | Ensemble Relational Learning based on Selective Propositionalization | cs.LG | Dealing with structured data needs the use of expressive representation
formalisms that, however, puts the problem to deal with the computational
complexity of the machine learning process. Furthermore, real world domains
require tools able to manage their typical uncertainty. Many statistical
relational learning appro... | computer science |
4,761 | Clustering Markov Decision Processes For Continual Transfer | cs.AI | We present algorithms to effectively represent a set of Markov decision
processes (MDPs), whose optimal policies have already been learned, by a
smaller source subset for lifelong, policy-reuse-based transfer learning in
reinforcement learning. This is necessary when the number of previous tasks is
large and the cost o... | computer science |
4,762 | A hybrid decision support system : application on healthcare | cs.AI | Many systems based on knowledge, especially expert systems for medical
decision support have been developed. Only systems are based on production
rules, and cannot learn and evolve only by updating them. In addition, taking
into account several criteria induces an exorbitant number of rules to be
injected into the syst... | computer science |
4,763 | Ranking Algorithms by Performance | cs.AI | A common way of doing algorithm selection is to train a machine learning
model and predict the best algorithm from a portfolio to solve a particular
problem. While this method has been highly successful, choosing only a single
algorithm has inherent limitations -- if the choice was bad, no remedial action
can be taken ... | computer science |
4,764 | One-Class Classification: Taxonomy of Study and Review of Techniques | cs.LG | One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been... | computer science |
4,765 | Improving circuit miniaturization and its efficiency using Rough Set
Theory | cs.LG | High-speed, accuracy, meticulousness and quick response are notion of the
vital necessities for modern digital world. An efficient electronic circuit
unswervingly affects the maneuver of the whole system. Different tools are
required to unravel different types of engineering tribulations. Improving the
efficiency, accu... | computer science |
4,766 | A Methodology for Player Modeling based on Machine Learning | cs.AI | AI is gradually receiving more attention as a fundamental feature to increase
the immersion in digital games. Among the several AI approaches, player
modeling is becoming an important one. The main idea is to understand and model
the player characteristics and behaviors in order to develop a better AI. In
this work, we... | computer science |
4,767 | Scalable Planning and Learning for Multiagent POMDPs: Extended Version | cs.AI | Online, sample-based planning algorithms for POMDPs have shown great promise
in scaling to problems with large state spaces, but they become intractable for
large action and observation spaces. This is particularly problematic in
multiagent POMDPs where the action and observation space grows exponentially
with the numb... | computer science |
4,768 | Extreme State Aggregation Beyond MDPs | cs.AI | We consider a Reinforcement Learning setup where an agent interacts with an
environment in observation-reward-action cycles without any (esp.\ MDP)
assumptions on the environment. State aggregation and more generally feature
reinforcement learning is concerned with mapping histories/raw-states to
reduced/aggregated sta... | computer science |
4,769 | PGMHD: A Scalable Probabilistic Graphical Model for Massive Hierarchical
Data Problems | cs.AI | In the big data era, scalability has become a crucial requirement for any
useful computational model. Probabilistic graphical models are very useful for
mining and discovering data insights, but they are not scalable enough to be
suitable for big data problems. Bayesian Networks particularly demonstrate this
limitation... | computer science |
4,770 | 'Almost Sure' Chaotic Properties of Machine Learning Methods | cs.LG | It has been demonstrated earlier that universal computation is 'almost
surely' chaotic. Machine learning is a form of computational fixed point
iteration, iterating over the computable function space. We showcase some
properties of this iteration, and establish in general that the iteration is
'almost surely' of chaoti... | computer science |
4,771 | Domain-Independent Optimistic Initialization for Reinforcement Learning | cs.LG | In Reinforcement Learning (RL), it is common to use optimistic initialization
of value functions to encourage exploration. However, such an approach
generally depends on the domain, viz., the scale of the rewards must be known,
and the feature representation must have a constant norm. We present a simple
approach that ... | computer science |
4,772 | Where do goals come from? A Generic Approach to Autonomous Goal-System
Development | cs.LG | Goals express agents' intentions and allow them to organize their behavior
based on low-dimensional abstractions of high-dimensional world states. How can
agents develop such goals autonomously? This paper proposes a detailed
conceptual and computational account to this longstanding problem. We argue to
consider goals ... | computer science |
4,773 | A Parallel and Efficient Algorithm for Learning to Match | cs.LG | Many tasks in data mining and related fields can be formalized as matching
between objects in two heterogeneous domains, including collaborative
filtering, link prediction, image tagging, and web search. Machine learning
techniques, referred to as learning-to-match in this paper, have been
successfully applied to the p... | computer science |
4,774 | A Comparison of learning algorithms on the Arcade Learning Environment | cs.LG | Reinforcement learning agents have traditionally been evaluated on small toy
problems. With advances in computing power and the advent of the Arcade
Learning Environment, it is now possible to evaluate algorithms on diverse and
difficult problems within a consistent framework. We discuss some challenges
posed by the ar... | computer science |
4,775 | Warranty Cost Estimation Using Bayesian Network | cs.AI | All multi-component product manufacturing companies face the problem of
warranty cost estimation. Failure rate analysis of components plays a key role
in this problem. Data source used for failure rate analysis has traditionally
been past failure data of components. However, failure rate analysis can be
improved by mea... | computer science |
4,776 | Falling Rule Lists | cs.AI | Falling rule lists are classification models consisting of an ordered list of
if-then rules, where (i) the order of rules determines which example should be
classified by each rule, and (ii) the estimated probability of success
decreases monotonically down the list. These kinds of rule lists are inspired
by healthcare ... | computer science |
4,777 | Efficient Algorithms for Bayesian Network Parameter Learning from
Incomplete Data | cs.LG | We propose an efficient family of algorithms to learn the parameters of a
Bayesian network from incomplete data. In contrast to textbook approaches such
as EM and the gradient method, our approach is non-iterative, yields closed
form parameter estimates, and eliminates the need for inference in a Bayesian
network. Our ... | computer science |
4,778 | The Computational Theory of Intelligence: Information Entropy | cs.AI | This paper presents an information theoretic approach to the concept of
intelligence in the computational sense. We introduce a probabilistic framework
from which computational intelligence is shown to be an entropy minimizing
process at the local level. Using this new scheme, we develop a simple data
driven clustering... | computer science |
4,779 | The Libra Toolkit for Probabilistic Models | cs.LG | The Libra Toolkit is a collection of algorithms for learning and inference
with discrete probabilistic models, including Bayesian networks, Markov
networks, dependency networks, and sum-product networks. Compared to other
toolkits, Libra places a greater emphasis on learning the structure of
tractable models in which e... | computer science |
4,780 | Detecting Falls with X-Factor Hidden Markov Models | cs.LG | Identification of falls while performing normal activities of daily living
(ADL) is important to ensure personal safety and well-being. However, falling
is a short term activity that occurs infrequently. This poses a challenge to
traditional classification algorithms, because there may be very little
training data for ... | computer science |
4,781 | Bridging belief function theory to modern machine learning | cs.AI | Machine learning is a quickly evolving field which now looks really different
from what it was 15 years ago, when classification and clustering were major
issues. This document proposes several trends to explore the new questions of
modern machine learning, with the strong afterthought that the belief function
framewor... | computer science |
4,782 | Exploring Bayesian Models for Multi-level Clustering of Hierarchically
Grouped Sequential Data | cs.LG | A wide range of Bayesian models have been proposed for data that is divided
hierarchically into groups. These models aim to cluster the data at different
levels of grouping, by assigning a mixture component to each datapoint, and a
mixture distribution to each group. Multi-level clustering is facilitated by
the sharing... | computer science |
4,783 | Optimal Nudging: Solving Average-Reward Semi-Markov Decision Processes
as a Minimal Sequence of Cumulative Tasks | cs.LG | This paper describes a novel method to solve average-reward semi-Markov
decision processes, by reducing them to a minimal sequence of cumulative reward
problems. The usual solution methods for this type of problems update the gain
(optimal average reward) immediately after observing the result of taking an
action. The ... | computer science |
4,784 | Use of Ensembles of Fourier Spectra in Capturing Recurrent Concepts in
Data Streams | cs.AI | In this research, we apply ensembles of Fourier encoded spectra to capture
and mine recurring concepts in a data stream environment. Previous research
showed that compact versions of Decision Trees can be obtained by applying the
Discrete Fourier Transform to accurately capture recurrent concepts in a data
stream. Howe... | computer science |
4,785 | Comparison of Training Methods for Deep Neural Networks | cs.LG | This report describes the difficulties of training neural networks and in
particular deep neural networks. It then provides a literature review of
training methods for deep neural networks, with a focus on pre-training. It
focuses on Deep Belief Networks composed of Restricted Boltzmann Machines and
Stacked Autoencoder... | computer science |
4,786 | Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks
Using a Polynomial Number of Score Evaluations | cs.LG | We introduce Selective Greedy Equivalence Search (SGES), a restricted version
of Greedy Equivalence Search (GES). SGES retains the asymptotic correctness of
GES but, unlike GES, has polynomial performance guarantees. In particular, we
show that when data are sampled independently from a distribution that is
perfect wit... | computer science |
4,787 | ASlib: A Benchmark Library for Algorithm Selection | cs.AI | The task of algorithm selection involves choosing an algorithm from a set of
algorithms on a per-instance basis in order to exploit the varying performance
of algorithms over a set of instances. The algorithm selection problem is
attracting increasing attention from researchers and practitioners in AI. Years
of fruitfu... | computer science |
4,788 | The Online Coupon-Collector Problem and Its Application to Lifelong
Reinforcement Learning | cs.LG | Transferring knowledge across a sequence of related tasks is an important
challenge in reinforcement learning (RL). Despite much encouraging empirical
evidence, there has been little theoretical analysis. In this paper, we study a
class of lifelong RL problems: the agent solves a sequence of tasks modeled as
finite Mar... | computer science |
4,789 | Fast Online Clustering with Randomized Skeleton Sets | cs.AI | We present a new fast online clustering algorithm that reliably recovers
arbitrary-shaped data clusters in high throughout data streams. Unlike the
existing state-of-the-art online clustering methods based on k-means or
k-medoid, it does not make any restrictive generative assumptions. In addition,
in contrast to exist... | computer science |
4,790 | Online Transfer Learning in Reinforcement Learning Domains | cs.AI | This paper proposes an online transfer framework to capture the interaction
among agents and shows that current transfer learning in reinforcement learning
is a special case of online transfer. Furthermore, this paper re-characterizes
existing agents-teaching-agents methods as online transfer and analyze one such
teach... | computer science |
4,791 | Emphatic Temporal-Difference Learning | cs.LG | Emphatic algorithms are temporal-difference learning algorithms that change
their effective state distribution by selectively emphasizing and
de-emphasizing their updates on different time steps. Recent works by Sutton,
Mahmood and White (2015), and Yu (2015) show that by varying the emphasis in a
particular way, these... | computer science |
4,792 | On the Computability of Solomonoff Induction and Knowledge-Seeking | cs.AI | Solomonoff induction is held as a gold standard for learning, but it is known
to be incomputable. We quantify its incomputability by placing various flavors
of Solomonoff's prior M in the arithmetical hierarchy. We also derive
computability bounds for knowledge-seeking agents, and give a limit-computable
weakly asympto... | computer science |
4,793 | A Weakly Supervised Learning Approach based on Spectral Graph-Theoretic
Grouping | cs.LG | In this study, a spectral graph-theoretic grouping strategy for weakly
supervised classification is introduced, where a limited number of labelled
samples and a larger set of unlabelled samples are used to construct a larger
annotated training set composed of strongly labelled and weakly labelled
samples. The inherent ... | computer science |
4,794 | Maintaining prediction quality under the condition of a growing
knowledge space | cs.AI | Intelligence can be understood as an agent's ability to predict its
environment's dynamic by a level of precision which allows it to effectively
foresee opportunities and threats. Under the assumption that such intelligence
relies on a knowledge space any effective reasoning would benefit from a
maximum portion of usef... | computer science |
4,795 | Lifted Representation of Relational Causal Models Revisited:
Implications for Reasoning and Structure Learning | cs.AI | Maier et al. (2010) introduced the relational causal model (RCM) for
representing and inferring causal relationships in relational data. A lifted
representation, called abstract ground graph (AGG), plays a central role in
reasoning with and learning of RCM. The correctness of the algorithm proposed
by Maier et al. (201... | computer science |
4,796 | Type-Constrained Representation Learning in Knowledge Graphs | cs.AI | Large knowledge graphs increasingly add value to various applications that
require machines to recognize and understand queries and their semantics, as in
search or question answering systems. Latent variable models have increasingly
gained attention for the statistical modeling of knowledge graphs, showing
promising r... | computer science |
4,797 | Artificial Prediction Markets for Online Prediction of Continuous
Variables-A Preliminary Report | cs.AI | We propose the Artificial Continuous Prediction Market (ACPM) as a means to
predict a continuous real value, by integrating a range of data sources and
aggregating the results of different machine learning (ML) algorithms. ACPM
adapts the concept of the (physical) prediction market to address the
prediction of real val... | computer science |
4,798 | Value function approximation via low-rank models | cs.LG | We propose a novel value function approximation technique for Markov decision
processes. We consider the problem of compactly representing the state-action
value function using a low-rank and sparse matrix model. The problem is to
decompose a matrix that encodes the true value function into low-rank and
sparse componen... | computer science |
4,799 | Reinforcement Learning with Parameterized Actions | cs.AI | We introduce a model-free algorithm for learning in Markov decision processes
with parameterized actions-discrete actions with continuous parameters. At each
step the agent must select both which action to use and which parameters to use
with that action. We introduce the Q-PAMDP algorithm for learning in these
domains... | computer science |
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