Prior knowledge for learning networks in non-probabilistic settings
نویسندگان
چکیده
منابع مشابه
Prior knowledge for learning networks in non-probabilistic settings
Current learning methods for general causal networks are basically data-driven. Exploration of the search space is made by resorting to some quality measure of prospective solutions. This measure is usually based on statistical assumptions. We discuss the interest of adopting a dierent point of view closer to machine learning techniques. Our main point is the convenience of using prior knowled...
متن کاملRational decisions in non-probabilistic settings
The knowledge-based rational decision model (KBR-model), developed in [1], offers an approach to rational decision making in a non-probabilistic setting, e.g., in perfect information games with deterministic payoffs. The KBR-model is an epistemically explicit form of standard game-theoretical assumptions, e.g., Harsanyi’s Maximin Postulate. This model suggests following maximin strategy over al...
متن کاملpragmatic knowledge in university entrance exam for english majors
abstract pragmatics is the study of communicative action in its sociocultural context. communicative action includes not only using speech acts (such as apologizing, complaining, complimenting, and requesting), but also engaging in different types of discourse and participating in speech events of varying length and complexity. the present study aimed to investigate the assessment of pragm...
15 صفحه اولProbabilistic Dialogue Models with Prior Domain Knowledge
Probabilistic models such as Bayesian Networks are now in widespread use in spoken dialogue systems, but their scalability to complex interaction domains remains a challenge. One central limitation is that the state space of such models grows exponentially with the problem size, which makes parameter estimation increasingly difficult, especially for domains where only limited training data is a...
متن کاملIntelligence, Prior Knowledge, and Learning
Intelligence test scores can account for achievement differences in many content areas to a considerable extent. An individual’s IQ results from complex interactions between genes and environmental stimulation, foremost schooling. The amount of variance in intelligence to be explained by genes is the higher the more successful a society is in providing cognitively stimulating environments for e...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Approximate Reasoning
سال: 2000
ISSN: 0888-613X
DOI: 10.1016/s0888-613x(99)00046-8