知識表示範例 的英文怎麼說

中文拼音 [zhīzhìbiǎoshìfànlì]
知識表示範例 英文
knowledge representation paradigm
  • 知 : Ⅰ動詞1 （知道） know; realize; be aware of 2 （使知道） inform; notify; tell 3 （舊指主管） admin...
  • 識 : 識Ⅰ動詞[書面語] （記） remember; commit to memory Ⅱ名詞1. [書面語] （記號） mark; sign 2. （姓氏） a surname
  • 表 : Ⅰ名詞1 （外面;外表） outside; surface; external 2 （中表親戚） the relationship between the child...
  • 示 : Ⅰ動詞（擺出或指出使人知道; 表明） show; indicate; signify; instruct; notify Ⅱ名詞1 [書面語] （給...
  • 範 : 名詞1 [書面語] （模子） pattern; mould; matrix 2 （模範; 榜樣） model; example 3 （范圍） boundar...
  • 知識 : 1 （認識和經驗的總和） knowledge; know how; science 2 （有關學術文化的） pertaining to learning o...
  • 表示 : show; express; mean; indicate; expression; presentation; signifying; remark; representation
  • 範例 : example; instance; model
  1. Neural network is applied in design of blast parameter, which is not needed the trim and sum up of engineer and knowledge of expertise. what are needed is some succeed examples and stylebooks to train the system. to some knowledge, which is expressed by implication, at the same time to acquire knowledge, much knowledge in the same question were showed in the same network

    採用神經網路進行爆破參數設計不需要知識工程師進行整理、總結以及消化領域專家的知識，只需要用領域專家解決問題的實例或範例來訓練網路；在知識表示方面，採取隱式表示；在知識獲取的同時，自動產生的知識由網路的結構和權值表示，通用性強，便於實現知識的自動獲取和并行聯想推理。
  2. Bn is network structure with clarity semantics. lt exploits the structure of the domain to allow a compact representation of complex joint probability distribution. its sound probabilistic semantics, explicit encoding of relevance relationships, inference algorithms and learning algorithms that are fairly efficient and effective in pratice, and decision - making mechanism of facility, have led bn to enter the artificial intelligence ( ai ) mainstream. for the reasons that they have produced more and more practical values and economic profits in many important application fields, such as modern expert systems, diagnosis engines, decision support systems, and data mining systems, researchers from both industry and academia are thus taking them much seriously

    它具有清晰語義的網路結構；它揭示領域對象的內在結構，是復雜全概率分佈的緊湊表示方式；其堅實的理論基礎、知識結構的自然表述方式、靈活的推理能力、方便的決策機制及有效的學習能力使其成為一種主要的不確定知識的處理方法。貝葉斯網路已經在專家系統、決策支持系統、數據挖掘系統和範例推理系統等許多重要領域產生應用價值和經濟效益。
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