1,052 research outputs found
Multiple Model Rao-Blackwellized Particle Filter for Manoeuvring Target Tracking
Particle filters can become quite inefficient when applied to a high-dimensional state space since a prohibitively large number of samples may be required to approximate the underlying density functions with desired accuracy. In this paper, a novel multiple model Rao-Blackwellized particle filter (MMRBPF)-based algorithm has been proposed for manoeuvring target tracking in a cluttered environment. The advantage of the proposed approach is that the Rao-Blackwellization allows the algorithm to be partitioned into target tracking and model selection sub-problems, where the target tracking can be solved by the probabilistic data association filter, and the model selection by sequential importance sampling. The analytical relationship between target state and model is exploited to improve the efficiency and accuracy of the proposed algorithm. Moreover, to reduce the particle-degeneracy problem, the resampling approach is selectively carried out. Finally, experiment results, show that the proposed algorithm, has advantages over the conventional IMM-PDAF algorithm in terms of robust and efficiency.Defence Science Journal, 2009, 59(3), pp.197-204, DOI:http://dx.doi.org/10.14429/dsj.59.151
Semantic Information G Theory and Logical Bayesian Inference for Machine Learning
An important problem with machine learning is that when label number n\u3e2, it is very difficult to construct and optimize a group of learning functions, and we wish that optimized learning functions are still useful when prior distribution P(x) (where x is an instance) is changed. To resolve this problem, the semantic information G theory, Logical Bayesian Inference (LBI), and a group of Channel Matching (CM) algorithms together form a systematic solution. MultilabelMultilabel A semantic channel in the G theory consists of a group of truth functions or membership functions. In comparison with likelihood functions, Bayesian posteriors, and Logistic functions used by popular methods, membership functions can be more conveniently used as learning functions without the above problem. In Logical Bayesian Inference (LBI), every label’s learning is independent. For Multilabel learning, we can directly obtain a group of optimized membership functions from a big enough sample with labels, without preparing different samples for different labels. A group of Channel Matching (CM) algorithms are developed for machine learning. For the Maximum Mutual Information (MMI) classification of three classes with Gaussian distributions on a two-dimensional feature space, 2-3 iterations can make mutual information between three classes and three labels surpass 99% of the MMI for most initial partitions. For mixture models, the Expectation-Maxmization (EM) algorithm is improved and becomes the CM-EM algorithm, which can outperform the EM algorithm when mixture ratios are imbalanced, or local convergence exists. The CM iteration algorithm needs to combine neural networks for MMI classifications on high-dimensional feature spaces. LBI needs further studies for the unification of statistics and logic
Connectionist Inference Models
The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling
Envelopes of conditional probabilities extending a strategy and a prior probability
Any strategy and prior probability together are a coherent conditional
probability that can be extended, generally not in a unique way, to a full
conditional probability. The corresponding class of extensions is studied and a
closed form expression for its envelopes is provided. Then a topological
characterization of the subclasses of extensions satisfying the further
properties of full disintegrability and full strong conglomerability is given
and their envelopes are studied.Comment: 2
Segmenting the heterogeneity of tourist preferences using a latent class model combined with the EM algorithm
An important component of conjoint analysis is market segmentation where the main objective is to address the heterogeneity of consumer preferences. Latent class methodology is one of the several conjoint segmentation procedures that overcome the limitations of aggregate analysis and a-priori segmentation. The main benefit of Latent class models is that they simultaneously estimate market segment membership and parameter estimates for each derived market segment. In this paper we present two latent class models. The first model is a latent class metric model using mixtures of multivariate conditional normal distributions to analyze rating data. The second is a latent class multinomial logit model used to analyze choice data. The EM algorithm is employed to maximize the likelihood in both models. The application focuses on tourists’ preference and choice behaviour when assessing package tours. A number of demographic and product related explanatory variables are used to generate segments that are accessible and actionable. A Monte Carlo study is also presented in this paper. This study examines how the number of hypothetical subjects, number of specified segments and number of predictors affect the performance of the latent class metric conjoint model with respect to parameter recovery and segment membership recovery.peer-reviewe
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Local search: A guide for the information retrieval practitioner
There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR
A fuzzified systematic adjustment of the robotic Darwinian PSO
The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle
Swarm Optimization using natural selection to enhance the ability to escape from sub-optimal solutions.
An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic
Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots,
hence decreasing the amount of required information exchange among robots. This paper further extends
the previously proposed algorithm adapting the behavior of robots based on a set of context-based
evaluation metrics. Those metrics are then used as inputs of a fuzzy system so as to systematically
adjust the RDPSO parameters (i.e., outputs of the fuzzy system), thus improving its convergence rate,
susceptibility to obstacles and communication constraints. The adapted RDPSO is evaluated in groups
of physical robots, being further explored using larger populations of simulated mobile robots within a
larger scenario
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