This paper contributes to the research on Learning in Databases in two ways. First, the concept of an inductive relation is introduced, as a natural development of other forms of intensional information, such as views...
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This paper contributes to the research on Learning in Databases in two ways. First, the concept of an inductive relation is introduced, as a natural development of other forms of intensional information, such as views and relations defined deductively. Second, a class of top-down methods for computing such inductive relations is analyzed, and major problems produced by recursive and interdependent relations are considered.
This software reuse system helps a user build programs by reusing modules stored in an existing library. The system, dubbed caesar (Case-basEd SoftwAre Reuse), is conceived in the case-based reasoning framework, where...
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The available concept-learners only partially fulfill the needs imposed by the learning apprentice generation of learners. We present a novel approach to inter-active concept-learning and constructive induction that b...
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The available concept-learners only partially fulfill the needs imposed by the learning apprentice generation of learners. We present a novel approach to inter-active concept-learning and constructive induction that better fits the requirements imposed by the learning apprentice paradigm. The approach is incorporated in the system Clint-Cia, which integrates several user-friendly features into one working whole: it is interactive, generates examples, shifts its bias, identifies concepts in the limit, copes with indirect relevance, recovers from errors, performs constructive induction and invents new concepts by analogy to previously learned ones.
Inference is a very general reasoning process that allows us to draw consequences from some body of knowledge. Machine learning (ML) uses the three kinds of possible inferences, deductive, inductive, and analogical. W...
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Inference is a very general reasoning process that allows us to draw consequences from some body of knowledge. Machine learning (ML) uses the three kinds of possible inferences, deductive, inductive, and analogical. We describe here different methods, using these inferences, that have been created during the last decade to improve the way machines can learn. We have already presented the most classical approaches in a book (Kodratoff, 1988), and in several review papers (Kodratoff, 1989, 1990a, 1992). These results will be described here very briefly, in order to leave room for newer results. We include also genetic algorithms as an induction technique. We restrict our presentation to the symbolic aspects of connectionism. A learning system can also be viewed as a mechanism skimming interesting knowledge out of the flow of information that runs through it. We present several existing learning systems from this point of view.
To enable the treatment of hepatic metastasis with higher, theoretically more effective, doses of systemically toxic anticancer drugs, an isolated liver perfusion (ILP) technique was developed in WAG/Ola rats. First, ...
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To enable the treatment of hepatic metastasis with higher, theoretically more effective, doses of systemically toxic anticancer drugs, an isolated liver perfusion (ILP) technique was developed in WAG/Ola rats. First, in a toxicity study the maximally tolerated dose (MTD) of mitomycin C (MMC) was determined for a 25-min ILP and for hepatic artery infusion (HAI) after the administration of a bolus dose. The MTD in the ILP setting (4.8 mg/kg) was 4 times that using HAI (1.2 mg/kg). Subsequently, in a rat colorectal hepatic-metastasis model, concentrations of MMC in tumour, liver, plasma and perfusate were measured during a 25-min ILP to investigate the expected pharmacokinetic advantage of ILP. The mean plasma level determined after ILP (1.2 as well as 4.8 mg/kg MMC) was significantly lower (P < 0.001) than that obtained following HAI. This may explain both the absence of severe systemic toxicity and the higher MTD in ILP-treated groups. No significant difference in mean tumour and liver tissue concentrations of MMC were found when the groups treated with 1.2 mg/kg drug via HAI vs ILP were compared. The mean MMC concentration in tumour tissue was significantly higher (almost 5 times;P < 0.05) in rats treated by ILP with the MTD (4.8 mg/kg) than in those treated via HAI with the MTD (1.2 mg/kg). ILP of MMC can be safely performed using a dose 4 times higher than the MTD in the HAI setting, leading to an almost 5-fold concentration of MMC in hepatic metastasis. ILP of MMC may therefore represent a promising therapy for metastasis confined to the liver.
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