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ImageJ Scriptable Java app for scientific image processing. Download ImageJ d 1. Free Download for Mac. User reviews about ImageJ Review. Laws concerning the use of this software vary from country to country. Be more productive, more aware, more prepared, more informed, more competitive, and more educated by using predictive models in your problem domain.
Update your models as you have new data available with the same ease-of-use and speed of model building. Read more This is where Similar Patterns self-organizing modeling technology comes in.
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Similar Patterns - which is also known as Analog Complexing - can be seen as a sequential pattern recognition method that predicts and qualitatively explains fuzzy processes inherently. This method is based on the assumption that every most recent period of time reference pattern of a given multi-dimensional time process has one ore more analogous periods in history similar patterns.
If so, a forecast of the reference pattern can be obtained by transforming the known continuations of the similar patterns in history into continuations of the present state of the process. This means the observed. The Similar Patterns method implemented in Insights has been enhanced by an inductive, self-organizing modeling approach and by an advanced selection procedure to make it applicable to evolutionary non-stationary time processes, too.
Use this unique modeling technology to forecast complex and very noisy time processes such as market prices, market demand, or sales figures instantly and with almost no efforts. Optimize the similar patterns composite manually or automatically to get improved or more reasonable forecasts.
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Once you have generated a Similar Patterns model you can update it on new data stored in Excel via the Autoupdate function of Insights , automatically. Run simulations on historical data continuously for backtesting your model, for example, or use it on real-time data for instant forecasting. All results are reported back and stored in Excel for further application.
A number of examples is included in the free software download. About KnowledgeMiner We are passionate about developing high-quality, cutting-edge, knowledge mining software tools which add value to our customers' fields as well as to society in general. Why do I need a model? Models form the basis of decisions. Today, Data Mining is mostly recognized as the process of collecting and analyzing amounts of personal data of customers and people for different purposes by an increasing number of companies, governments and public services.
However, Data Mining or better its next generation, Knowledge Mining, is much more than this and can be much more useful in various problem fields. It can be very helpful, valuable, and even sometimes an unavoidable tool for finding solutions and getting insights into many real-world processes.
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It is worth building models to aid decision making, because models make it possible to: Identify the relationships between cause and effect. This leads to a deeper understanding of the problem at hand by deriving an analytical relationship between them, Predict the respective objects can expect over a finite future time span, but also to experiment with models. Exactly the ability to make predictions about the future forms the core of intelligence at all.
Simulate the objects' behaviour by experiment with models, and thus answer "what-if" questions essential to decision-making, Control the objects by finding suitable means to effect the objects and enforce a specific behaviour. The world around us is getting more complex, more interdependent, more connected and global.
Uncertainty and vagueness, coupled with rapid developments radically affect humanity. Though we observe these effects, we most often do not understand the consequences of any actions, the dynamics involved and the inter-dependencies of real-world systems in which system variables are dynamically related to many others, and where it is usually difficult to differentiate which are the causes and which are the effects. Self-organizing Modeling. Discover new knowledge.
Gain insights. Self-organizing modeling extracts the information and knowledge necessary to build up a model from the provided data, only. It therefore transforms information into knowledge. It is based on the principle of induction which is composed of these three ideas: The principle of self-organization for adaptively evolving a network model without subjective points given; The principle of external information to allow objective selection of a model of optimal complexity, and The principle of regularization of ill-posed tasks.
Self-organizing modeling works on adaptive networks. Self-organization is considered in identifying connections between the network units by a learning mechanism to represent discrete items. For this approach, the objective is to estimate networks of relevant and sufficient size with a structure evolving during the estimation process.
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A process is said to undergo self-organization if identification emerges through the system's environment. This approach developed by A. Ivakhnenko is considered today as one of the first implementations of so-called Deep Learning.
Self-organization of optimal complex models from data in Insights along with analytical descriptions in implicit and explicit format. Interdependent Systems of Equations. Ex ante forecasting made easy. Real-world systems are described by a number of system parameters. The atmosphere, for example, consists of nitrogen, oxygen, ozone, aerosols, water vapor, CO2 and other substances and is influenced by the oceans and cosmic forces like sun irradiance.
These system parameters of the atmosphere can be divided into internal state endogenous variables of the system and external exogenous variables such as the influence of the sun. Linear, time invariant, dynamic systems can be described mathematically as systems of algebraic or difference equations as follows: Self-organized interdependent system of equations for a national economy ready for multi-step ex ante forecasting. Similar Patterns and Autoupdating Forecasts.
Forecast complex time processes easily. Complex, fuzzy, or very noisy processes are very hard to model and predict by known parametric modeling and data mining technologies. According to Stafford Beer's adequacy law, a process or object can only be described successfully if the modeling method used is adequate to the process it describes in terms of its formalization power or language.
Simple or deterministic processes can be described by deterministic modeling approaches, fuzzy processes need a more fuzzy explanation by models. This means the observed process itself is used to forecast its most recent state by a nonparametric approach. Forecasting bike sharing activity by similar patterns 30 hours ahead. Manually excluding a similar pattern white squares from the pattern composite for forecasting.
Autoupdating of Similar Patterns models. Forecast intervals of up to 5-steps-ahead forecasts are reported to Excel. Multi-level Model Validation. Innovation for reliable predictive models. Obtaining a model from data is easy. Obtaining a model from data that reliably reflects the underlying relationship in the data with some certainty is hard work. This is especially true for noisy, disturbed data. Noisy data are everywhere so you most probably will use them.
To systematically avoid overfitting - that is, when the model fits to random cases noise and therefore can only have poor predictive power, which makes the model useless -, to get optimized transfer functions in Active Neurons, and to self-organize robust optimal complex models with optimal predictive power, Insights employs original concepts of model validation at different levels of the self-organizing modeling process.
Together with our Live Prediction Validation, application of data mining models is now more reliable, stable, and valuable than ever before. Composite Models and Model Uncertainty. Do not believe a single model. Reality is fuzzy. Any single model describes reality incomplete. On the other hand, by principle in modeling, there is always a set of models that show comparable model performance with respect to a certain quality criterion such as closeness-of-fit but that use different input variables and different model descriptions.
So why prefer one model against the other ones and use just this one?
More advantaguous and valuable is grouping a set of models into a composite model or model ensemble. Composite models describe reality more thoroughly, increase the prediction accuracy of the composite, and provide a case-by-case prediction interval or prediction uncertainty by low, high, and most likely prediction values. This also opens up the path to what-if scenarios. In KnowledgeMiner Insights modeling always results in a model composite of most appropriate individual models.
You use a composite as you use any single model. Exclude, re-insert or delete a model from the composite and optimize the power and behavior of the composite. Model uncertainty and prediction interval Example: Individual models of a composite with models excluded from prediction calculation.
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