Text Summarization for Knowledge Inference and Biological Validation:The BioSumm approach Lorenzo Montrucchio
Neural Networks for Knowledge Representation and Inference: Daniel S. Levine
Sensor Data Distribution and Distributed Knowledge Inference Systems: Nilamadhab Mishra/ Hsien-Tsung Chang/ Chung-Chih Lin
According to David Hume, the concept of causation and probability are to be understood in terms of the concepts of similarity and repetition. In this book, it is shown that they are to be understood in terms of the concept of continuity. One corollary is that there is no legitimate basis for skepticism concerning the legitimacy of inductive inference. Another is that anti-realism about theoretical entities is misconceived. 1. Language: English. Narrator: John-Michael Kuczynski. Audio sample: http://samples.audible.de/bk/acx0/071346/bk_acx0_071346_sample.mp3. Digital audiobook in aax.
A compendium of some of Kuczynski´s more readable work on philosophy, scientific methodology, and the theory of knowledge. 1. Language: English. Narrator: J.-M Kuczynski. Audio sample: http://samples.audible.de/bk/acx0/092935/bk_acx0_092935_sample.mp3. Digital audiobook in aax.
Decision making under uncertainty can be formulated as the diagnostic problem. It consists in finding a value of a diagnosis variable on the basis of concrete values of some symptom variables. The link between the diagnosis and symptoms is supposed not to be a strict functional dependence (e.g.implications), but there is ´´certain uncertainty´´ involved. One of the theoretical approaches is based on the so called marginal problem where background model is probability. The ´´knowledge base´´ for an inference engine (i.e. algorithm performing the knowledge integration) is formed by a given set of less-dimensional distributions. They should be provided by experts in the problem area. The basic idea is to construct an approximation of the theoretical joint distribution (and especially its conditional probabilities ) from the knowledge base whose distributions are supposed to be marginals of the joint distribution. The topic of the book is selecting the marginals in an automated way (without experts) if a statistical data file from the problem area is available. The resulting data base should be in certain way optimal. The six algorithms presented in the book may serve for the purpose.
The philosophy of historiography examines our representations and knowledge of the past, the relation between evidence, inference, explanation and narrative. Do we possess knowledge of the past? Do we just have probable beliefs about the past, or is historiography a piece of convincing fiction? The philosophy of history is the direct philosophical examination of history, whether it is necessary or contingent, whether it has a direction or whether it is coincidental, and if it has a direction, what it is, and how and why it is unfolding? The fifty entries in this companion cover the main issues in the philosophies of historiography and history, including natural history and the practices of historians. Written by an international and multi–disciplinary group of experts, these clearly written entries present a cutting–edge updated picture of current research in the philosophies of historiography and history. This companion will be of interest to philosophers, historians, natural historians, and social scientists. Aviezer Tucker is a Gvirtzman Memorial Foundation Fellow and teaches at the CEVRO Institute in Prague. He held research positions at the Australian National University, New York University, Columbia University and the Central European University in Prague. He is the author of Our Knowledge of the Past: A Philosophy of Historiography (2004) and a past president of the Society for the Philosophy of History. 1. Language: English. Narrator: Mary Kane. Audio sample: http://samples.audible.de/bk/adbl/008916/bk_adbl_008916_sample.mp3. Digital audiobook in aax.