183316
Modifying Bayesian Networks by Probability Constraints
This paper deals with the following problem: modify a Bayesian network to satisfy a given set of probability constraints by only change its conditional probability tables, and the probability distribution of the resulting network should be as close as possible to that of the original network. We propose to solve this problem by extending IPFP (iterative proportional fitting procedure) to probability distributions represented by Bayesian networks. The resulting algorithm E-IPFP is further developed to D-IPFP, which reduces the computational cost by decomposing a global E-IPFP into a set of smaller local E-IPFP problems. Limited analysis is provided, including the convergence proofs of the two algorithms. Computer experiments were conducted to validate the algorithms. The results are consistent with the theoretical analysis.
207155768
Swoogle: a search and metadata engine for the semantic web
Swoogle is a crawler-based indexing and retrieval system for the Semantic Web. It extracts metadata for each discovered document, and computes relations between documents. Discovered documents are also indexed by an information retrieval system which can use either character N-Gram or URIrefs as keywords to find relevant documents and to compute the similarity among a set of documents. One of the interesting properties we compute is <i>ontology rank</i>, a measure of the importance of a Semantic Web document.
20234262
Chromatic equivalence classes of certain generalized polygon trees
Let P(G) denote the chromatic polynomial of a graph G. Two graphs G and H are chromatically equivalent, written G ~ H, if P(G) = P(H). Let ¢ denote the family of all generalized polygon trees with three interior regions. Xu (1994) showed that ¢ is a union of chromatic equivalence classes under the equivalence relation ',-/. In this paper, we determine infinitely many chromatic equivalence classes in ~ under ',-~'. As a byproduct, we obtain a family of chromatically unique graphs established by Peng (1995).
5372255
Holes in Graphs
The celebrated Regularity Lemma of Szemeredi asserts that every sufficiently large graph $G$ can be partitioned in such a way that most pairs of the partition sets span $\epsilon$-regular subgraphs. In applications, however, the graph $G$ has to be dense and the partition sets are typically very small. If only one $\epsilon$-regular pair is needed, a much bigger one can be found, even if the original graph is sparse. In this paper we show that every graph with density $d$ contains a large, relatively dense $\epsilon$-regular pair. We mainly focus on a related concept of an $(\epsilon,\sigma)$-dense pair, for which our bound is, up to a constant, best possible.
16284083
Minimizing Complexity in Cellular Automata Models of Self-Replication
Understanding self-replication from an information processing perspective is important because, among other things, it can shed light on molecular mechanisms of biological reproduction and on prebiotic chemical evolution. Intuition, biological knowledge, and early computational models of self-replication all suggested that self-replication is an inherently complex process. In this paper we describe recent computational studies that challenge this viewpoint. We summarize our recent work with cellular automata models of simple yet non-trivial self-replicating structures called unsheathed loops. For example, one unsheathed loop consists of only six components and requires only 20 rules to specify the local intercomponent interactions needed to bring about replication. The implication of this work is that, when viewed as an emergent property of numerous local, concurrent interactions between components, self-replicating systems can be substantially simpler than is generally recognized.
36490919
Estimating baseline distribution in proportional hazards cure models
Semiparametric proportional hazards cure models have been proposed recently for survival data from some clinical trials where cure is a possibility. However, it is known that the estimated baseline distribution in the proposed methods will not be proper if there is no restriction on the tail of the distribution, which leads to the identifiability problem. In this work, we investigate several methods that impose restrictions on the tail of the baseline distribution. A simulation study shows that the proposed methods are useful in reducing estimation errors in the cure models. The impact of the different methods on the estimation of regression parameters and survival probabilities of patients who are not cured is detailed in the paper. The results provide useful guidelines for practitioners to select appropriate estimation methods for the semiparametric cure model. The application of the results is illustrated with a real data set from a clinical trial of breast cancer.
17328688
Improved Dataset Characterisation for Meta-learning
This paper presents new measures, based on the induced decision tree, to characterise datasets for meta-learning in order to select appropriate learning algorithms. The main idea is to capture the characteristics of dataset from the structural shape and size of decision tree induced from the dataset. Totally 15 measures are proposed to describe the structure of a decision tree. Their effectiveness is illustrated through extensive experiments, by comparing to the results obtained by the existing data characteristics techniques, including data characteristics tool (DCT) that is the most wide used technique in meta-learning, and Landmarking that is the most recently developed method.