Topic_2B_Expert_Systems
... software products like an expert system shell The user group Can be a regular user, eg, an employee of the organization May be an irregular user such as bank customer Needs and characteristics of both categories of users need to be taken into account, particularly for the user interface design. ...
... software products like an expert system shell The user group Can be a regular user, eg, an employee of the organization May be an irregular user such as bank customer Needs and characteristics of both categories of users need to be taken into account, particularly for the user interface design. ...
The Size of MDP Factored Policies
... the initial state and the actions executed so far. Namely, the aim of planning in a nondeterministic domain is that of determining a set of actions to execute that result in the best possible states (according to the reward function). Since the result of actions is not known for sure, we can only de ...
... the initial state and the actions executed so far. Namely, the aim of planning in a nondeterministic domain is that of determining a set of actions to execute that result in the best possible states (according to the reward function). Since the result of actions is not known for sure, we can only de ...
Overfitting and Model Selection
... The squared bias represents the extent to which the average prediction over all data sets differs from the desired regression function. The variance measures the extent to which the solutions for individual data sets vary around their average, the extent to which the function f (x; D) is sensitive t ...
... The squared bias represents the extent to which the average prediction over all data sets differs from the desired regression function. The variance measures the extent to which the solutions for individual data sets vary around their average, the extent to which the function f (x; D) is sensitive t ...
artificial neural network circuit for spectral pattern recognition
... with more neurons and layers the speed of the software ANN starts falling rapidly. And from the applicability point of view, using one processor board to implement a single neural network is too expensive. ANNs implemented in hardware are more efficient because of the parallel processing capabilitie ...
... with more neurons and layers the speed of the software ANN starts falling rapidly. And from the applicability point of view, using one processor board to implement a single neural network is too expensive. ANNs implemented in hardware are more efficient because of the parallel processing capabilitie ...
PMAPh_Kirke_AISB_final6
... Previous work on unconventional computing and music has focused on using unconventional computation methods as engines for new modes of musical expression. For example using in vitro neural networks [1] or slime molds [2] to drive a sound synthesizer. The research has not focused on studying the com ...
... Previous work on unconventional computing and music has focused on using unconventional computation methods as engines for new modes of musical expression. For example using in vitro neural networks [1] or slime molds [2] to drive a sound synthesizer. The research has not focused on studying the com ...
Statistics for Business and Economics Describing Data: Numerical 1
... each of the police officers in his division. He finds that the mean number of citations written by each officer is 23.2 citations per day, with a standard deviation of 3.1. Assume that the distribution of the number of tickets issued is approximately bell-shaped. 25) Which of the following statement ...
... each of the police officers in his division. He finds that the mean number of citations written by each officer is 23.2 citations per day, with a standard deviation of 3.1. Assume that the distribution of the number of tickets issued is approximately bell-shaped. 25) Which of the following statement ...
The DARPA High Performance Knowledge Bases
... 1173). We must, therefore, augment high-power knowledge-based systems, which give specific and precise answers, with weaker but adequate knowledge and inference. The inference methods might not all be sound and complete. Indeed, one might need a multitude of methods to implement what Polya called pl ...
... 1173). We must, therefore, augment high-power knowledge-based systems, which give specific and precise answers, with weaker but adequate knowledge and inference. The inference methods might not all be sound and complete. Indeed, one might need a multitude of methods to implement what Polya called pl ...
Developing Backward Chaining Algorithm of Inference Engine in
... knowledge expert or knowledge engineer can reference rules or facts that have not yet been created. It seems to be a simple and an instant work. The problem due to the performance of the knowledge will not occur until the number of rules is getting higher. Some problem may appear in the form of inco ...
... knowledge expert or knowledge engineer can reference rules or facts that have not yet been created. It seems to be a simple and an instant work. The problem due to the performance of the knowledge will not occur until the number of rules is getting higher. Some problem may appear in the form of inco ...
Time series
A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Examples of time series are ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, intelligent transport and trajectory forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called ""time series analysis"", which focuses on comparing values of a single time series or multiple dependent time series at different points in time.Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies, in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values (see time reversibility.)Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language.).