標(biāo)題: Titlebook: Connectionistic Problem Solving; Computational Aspect Steven E. Hampson Book 1990 Birkh?user Boston 1990 Extension.artificial intelligence. [打印本頁] 作者: brachytherapy 時間: 2025-3-21 16:31
書目名稱Connectionistic Problem Solving影響因子(影響力)
書目名稱Connectionistic Problem Solving影響因子(影響力)學(xué)科排名
書目名稱Connectionistic Problem Solving網(wǎng)絡(luò)公開度
書目名稱Connectionistic Problem Solving網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Connectionistic Problem Solving被引頻次
書目名稱Connectionistic Problem Solving被引頻次學(xué)科排名
書目名稱Connectionistic Problem Solving年度引用
書目名稱Connectionistic Problem Solving年度引用學(xué)科排名
書目名稱Connectionistic Problem Solving讀者反饋
書目名稱Connectionistic Problem Solving讀者反饋學(xué)科排名
作者: 違法事實(shí) 時間: 2025-3-21 20:27 作者: 單調(diào)性 時間: 2025-3-22 00:31 作者: 忍受 時間: 2025-3-22 04:57 作者: Vo2-Max 時間: 2025-3-22 12:27 作者: Etymology 時間: 2025-3-22 15:04 作者: Etymology 時間: 2025-3-22 18:10 作者: 我怕被刺穿 時間: 2025-3-22 22:09
Stimulus-Stimulus Associations and Parallel Search,embling action sequences which have quite different time/space characteristics. The best developed alternative to the S-R model is the stimulus-stimulus (S-S) model. In the S-S model the learned associations are between stimuli, rather than between stimulus and response (and stimulus and evaluation)作者: mechanical 時間: 2025-3-23 04:33 作者: 案發(fā)地點(diǎn) 時間: 2025-3-23 06:20
Stimulus-Goal Associations,se, but a goal. To take perhaps the simplest example, finger withdrawal was classically conditioned to a tone, using shock from a flat electrode as the US (Wickens, 1938). After training in a palm-down position, the response was tested in a palm-up position. The result was that the conditioned respo作者: 鞭子 時間: 2025-3-23 11:27 作者: LATHE 時間: 2025-3-23 16:41
Book 1990aces a path from relatively low-level neural/connectionistic structures and processes to relatively high-level animal/artificial intelligence behaviors. Incremental extension of this initial path permits increasingly sophisticated representation and processing strategies, and consequently increasing作者: DEFT 時間: 2025-3-23 19:43
Introduction,al chapters develop the basic components of the system at the node and network level, with the general goal of efficient category learning and representation. The later chapters are more concerned with the problems of assembling sequences of actions in order to achieve a given goal state.作者: 接觸 時間: 2025-3-24 00:07 作者: 一起平行 時間: 2025-3-24 03:13 作者: 成份 時間: 2025-3-24 09:00
Improving on Perceptron Training,is considered, and three techniques are developed which can significantly improve performance. These are variable associability, adaptive origin placement, and short-term weight modification. Each addresses a different limiting aspect of perceptron training.作者: 晚來的提名 時間: 2025-3-24 12:55
Turing Machines with Sublogarithmic Spaceal chapters develop the basic components of the system at the node and network level, with the general goal of efficient category learning and representation. The later chapters are more concerned with the problems of assembling sequences of actions in order to achieve a given goal state.作者: 橫條 時間: 2025-3-24 18:42
Turing Machines with Sublogarithmic Space. In this chapter, two S-S models of “planning” by parallel search are developed. The first searches backward from the goal state looking for a sequence of states that connect the goal state with the current state. The second searches forward from the current state looking for a path to the goal state.作者: LVAD360 時間: 2025-3-24 21:57 作者: Override 時間: 2025-3-24 23:30 作者: 旋轉(zhuǎn)一周 時間: 2025-3-25 03:19
Turing Machines with Sublogarithmic Spacectness of the preceding action (sometimes referred to as “l(fā)earning with a critic” (Widrow et al., 1973; Barto et al., 1981)). A specialized evaluation system is developed in this chapter and used to train the operators.作者: 外面 時間: 2025-3-25 10:43
Lecture Notes in Computer Sciencereme, since the S-S model alone is sufficient for adaptive behavior, it has been suggested that the S-R model is dispensable (Bolles, 1972; Bindra, 1976). This chapter completes the development of the S-S model by considering some of the biological evidence for and against such mechanisms.作者: 不能根除 時間: 2025-3-25 14:02
Languages acceptable with logarithmic space,ing that they had not learned a particular motor pattern, but the more general goal of traversing the maze to reach the goal box. The evidence for “place learning” as opposed to “response learning” which was discussed in the previous chapter is also evidence for some form of goal setting.作者: pacifist 時間: 2025-3-25 18:00
Node Structure and Training, extent. Both the node representation and the training algorithm are sufficiently simple to permit a significant amount of analysis. Variations on the basic model are considered and possible extensions to deal with continuous input values are explored.作者: 誹謗 時間: 2025-3-25 21:05 作者: 遺傳 時間: 2025-3-26 00:09
Stimulus-Stimulus Discussion,reme, since the S-S model alone is sufficient for adaptive behavior, it has been suggested that the S-R model is dispensable (Bolles, 1972; Bindra, 1976). This chapter completes the development of the S-S model by considering some of the biological evidence for and against such mechanisms.作者: Limited 時間: 2025-3-26 06:17
Stimulus-Goal Associations,ing that they had not learned a particular motor pattern, but the more general goal of traversing the maze to reach the goal box. The evidence for “place learning” as opposed to “response learning” which was discussed in the previous chapter is also evidence for some form of goal setting.作者: prolate 時間: 2025-3-26 10:26
ucture, traces a path from relatively low-level neural/connectionistic structures and processes to relatively high-level animal/artificial intelligence behaviors. Incremental extension of this initial path permits increasingly sophisticated representation and processing strategies, and consequently 作者: GUILT 時間: 2025-3-26 13:20 作者: 移動 時間: 2025-3-26 19:22
https://doi.org/10.1007/978-1-4684-6770-3Extension; artificial intelligence; behavior; intelligence; learning; memory; modeling; problem solving; pro作者: Lamina 時間: 2025-3-26 21:19
978-0-8176-3450-6Birkh?user Boston 1990作者: Apoptosis 時間: 2025-3-27 02:10
Operator Training, been made, but current connectionistic learning algorithms (e.g., Hinton et al., 1984; Ackley et al., 1985; Barto, 1985; Rumelhart et al., 1986) are of limited physiological relevance, and empirically are quite slow. The approaches developed here are somewhat more physiologically plausible and considerably faster.作者: 鞭子 時間: 2025-3-27 09:20
Turing Machines with Sublogarithmic Spacel neural/connectionistic structures and processes to relatively high-level animal/artificial intelligence behaviors. Incremental extension of this initial path permits increasingly sophisticated representation and processing strategies, and consequently increasingly sophisticated behavior. The initi作者: 橢圓 時間: 2025-3-27 09:58
https://doi.org/10.1007/3-540-58355-6old Logic Unit (TLU), or more specifically, a Linear Threshold Unit (LTU). The standard LTU is a thresholded linear equation that is used for the binary categorization of feature patterns. The primary learning process for a node is the perceptron training algorithm. Although neither the representati作者: Minuet 時間: 2025-3-27 17:30 作者: SPER 時間: 2025-3-27 20:15 作者: invade 時間: 2025-3-28 01:24
Lecture Notes in Computer Sciencepropriate response, or conversely, that each operator is a category detector for those conditions under which it should fire. Since the input features to an operator can be the output of any other node, this is consistent with both externally and internally generated behavior. The current domain is 作者: 厚顏 時間: 2025-3-28 04:29 作者: 預(yù)感 時間: 2025-3-28 10:00 作者: 彈藥 時間: 2025-3-28 14:14 作者: 鍍金 時間: 2025-3-28 17:02
Lecture Notes in Computer Scienceformation and use of S-R associations. Since it is not strictly necessary for the acquisition of appropriate behavior, it is interesting to ask whether all (or any) organisms actually use such a mechanism, and if so to what extent. There are at least two good reasons why some organisms may not. Firs作者: Cardiac-Output 時間: 2025-3-28 21:21
Languages acceptable with logarithmic space,se, but a goal. To take perhaps the simplest example, finger withdrawal was classically conditioned to a tone, using shock from a flat electrode as the US (Wickens, 1938). After training in a palm-down position, the response was tested in a palm-up position. The result was that the conditioned respo作者: SHRIK 時間: 2025-3-29 01:50 作者: Vldl379 時間: 2025-3-29 03:14 作者: Counteract 時間: 2025-3-29 09:01 作者: Scintillations 時間: 2025-3-29 11:38
Other models of Turing machines, single generalization hypothesis can make repeated mistakes on the same input patterns, a situation which need not occur with specific instance learning. Perceptron training is good at learning generalizations, but poor at learning specific instances.作者: inferno 時間: 2025-3-29 16:15 作者: Anticoagulants 時間: 2025-3-29 22:26 作者: GUILE 時間: 2025-3-30 03:19 作者: CHARM 時間: 2025-3-30 07:52
Learning and Using Specific Instances, single generalization hypothesis can make repeated mistakes on the same input patterns, a situation which need not occur with specific instance learning. Perceptron training is good at learning generalizations, but poor at learning specific instances.作者: 輕快走過 時間: 2025-3-30 09:45
Operator and Network Structure,t the individual neuron, but small assemblies of neurons whose combined activity can be functionally described. For example, the cortical column is one possible unit of description (Mountcastle, 1978, 1979; Hubel, 1981; Kohonen, 1984 ch. 8; Kuffler et al., 1984 ch. 3). In this chapter, the minimum s
