We address the problem of building a continuous occupancy representation of the environment with ranging sensors. Observations from such sensors provide two types of information: a line segment or a beam indicating no returns along them (free-space); a point or return at the end of the segment representing an occupied surface. To model these two types of observations in a principled statistical manner, we propose a novel methodology based on integral kernels. We show that integral kernels can be directly incorporated into a Gaussian process classification (GPC) framework to provide a continuous non-parametric Bayesian estimation of occupancy. Directly handling line segment and point observations avoids the need to discretise segments into points, reducing the computational cost of GPC inference and learning. We present experiments on 2D and 3D datasets demonstrating the benefits of the approach.

Semantic relatedness is the task of finding and quantifying Knowledge-based measures such as L&C (Leacock the strength of the semantic connections that exist between and Chodorow 1998), Lesk (Lesk 1986), Wu&Palmer (Wu textual units, be they word pairs, sentence pairs, or document and Palmer 1994), Resnik (Resnik 1995), J&C (Jiang and pairs. For instance, one may want to determine how Conrath 1997), H&S (Hirst and St Onge 1998), and many semantically related are car and automobile, ornoon and others, employ information extracted from manually constructed string. To make such a judgment, we rely on our accumulated lexical taxonomies like Wordnet (Fellbaum 1998), knowledge and experiences, and utilize our ability Roget (Jarmasz 2003), and Wiktionary (Zesch, Muller, and of conceptual thinking, abstraction, and generalization.

Evolutionary Algorithms (EA) are a branch of heuristic population-based optimization tools that is growing in popularity (especially for combinatorial and other problems with poorly understood landscapes). Despite their many uses, there are no proofs that an EA will always converge to the global optimum for any general problem.

Informed search algorithms such as A* use heuristics to focus exploration on states with low total path cost. To the extent that heuristics underestimate forward costs, a wider cost radius of suboptimal states will be explored. For many weighted graphs, however, a small distance in terms of cost may encompass a large fraction of the unweighted graph. We present a new informed search algorithm, Iterative Monotonically Bounded A* (IMBA*), which first proves that no optimal paths exist in a bounded cut of the graph before considering larger cuts. We prove that IMBA* has the same optimality and completeness guarantees as A* and, in a non-uniform pathfinding application, we empirically demonstrate substantial speed improvements over classic A*.

We introduce the level-ordered edge sequence (LOES), a suc- cinct encoding for state-sets based on prefix-trees. For use in state-space search, we give algorithms for member testing and element hashing with runtime dependent only on state- size, as well as space and memory efficient construction of and iteration over such sets. Finally we compare LOES to binary decision diagrams (BDDs) and explicitly packed set- representation over a range of IPC planning problems. Our results show LOES produces succinct set-encodings for a wider range of planning problems than both BDDs and ex- plicit state representation, increasing the number of problems that can be solved cost-optimally.

We seek informative representations of the processes underlying time series data. As a first step, we address problems in which these processes can be approximated by linear models that vary smoothly over time. To facilitate estimation of these linear models, we introduce a method of dimension reduction which significantly reduces error when models are estimated locally for each point in time. This improvement is gained by performing dimension reduction implicitly through the model parameters rather than directly in the observation space.

Some of these complex queries, such as Q1 or Q2, Recent advances in health and life sciences have led to generation can be represented in a formal query language (e.g., of a large amount of biomedical data. To facilitate access SQL/SPARQL) and then answered using Semantic Web to its desired parts, such a big mass of data has been represented technologies. However, queries, like Q4, that require auxiliary in structured forms, like biomedical ontologies and recursive definitions (such as transitive closure) cannot databases. On the other hand, representing these biomedical be directly represented in these languages; and thus such ontologies and databases in different forms, constructing queries cannot be answered directly using Semantic Web them independently from each other, and storing them at technologies. The experts usually compute auxiliary relations different locations have brought about many challenges for externally, for instance, by enumerating all drug-drug answering queries about the knowledge represented in these interaction chains or gene cliques, and then use these auxiliary ontologies and databases.

Bagging is a simple yet effective design which combines multiple single learners to form an ensemble for prediction. Despite its popular usage in many real-world applications, existing research is mainly concerned with studying unstable learners as the key to ensure the performance gain of a bagging predictor, with many key factors remaining unclear. For example, it is not clear when a bagging predictor can outperform a single learner and what is the expected performance gain when different learning algorithms were used to form a bagging predictor. In this paper, we carry out comprehensive empirical studies to evaluate bagging predictors by using 12 different learning algorithms and 48 benchmark data-sets. Our analysis uses robustness and stability decompositions to characterize different learning algorithms, through which we rank all learning algorithms and comparatively study their bagging predictors to draw conclusions. Our studies assert that both stability and robustness are key requirements to ensure the high performance for building a bagging predictor. In addition, our studies demonstrated that bagging is statistically superior to most single base learners, except for KNN and Naïve Bayes (NB). Multi-layer perception (MLP), Naïve Bayes Trees (NBTree), and PART are the learning algorithms with the best bagging performance.

This work focuses on developing domain-independent heuristics for probabilistic planning problems characterized by full observability and non-deterministic effects of actions that are expressed by probability distributions. The approach is to first search for a high probability deterministic plan using a classical planner. A novel probabilistic plan graph heuristic is used to guide the search towards high probability plans. The resulting plans can be used in a system that handles unexpected outcomes by runtime replanning. The plans can also be incrementally augmented with contingency branches for the most critical action outcomes. This abstract will describe the steps that we have taken in completing the above work and the obtained results.

In this paper, we present the exact online updating formulae for the generalized inverse of centered matrices. The computational cost is O ( mn ) for matrices of size m × n . Experimental results validate the proposed method’s accuracy and efficiency.