Error correction learning

There are basically four different approaches for training the nertral network. The first approach is the error correction learning rale, where the error between the output of the network and the measured output is used to adjust the network weights simultaneorrsly. A second approach is Boltzman learning, which is similar to error correction learning, however, the output of a neuron is based on a Boltzman statistical distribution. 

A few dozen grammar, spelling, punctuation, and capitalization mistakes account for the majority of common writing errors. Once you become acquainted with these common errors and learn how to avoid or correct them, your writing will greatly improve. Therefore, this section on mechanics will focus on the errors that occur most frequently. 

Dietterich, T.G., Bakiri, G. Solving multiclass learning problems via error-correcting output codes. 

This case study described model building for the prediction of metabolic lability of novel compounds. The analysis of different descriptors and machine learning algorithms shows that the chosen descriptor set, as well as the machine learning algorithm influence the predictivity of the model. Committee models, as implemented within Cubist, include an inherent error correction mechanism, which improves predictivity. 

The most common error while learning to write symbols and formulas is writing both letters in a two-letter elemental symbol as capitals. The first letter is always a capital letter. If a second letter is present, it is always written in lowercase. The language of chemistry is very precise, and correctly written symbols are part of that language. It is also important to leam the correct speUing of elemental names as you come to them. Flourine instead of fluorine is the most common misspelling of an elemental name. 

An observation of the results of cross-validation revealed that all but one of the compounds in the dataset had been modeled pretty well. The last (31st) compound behaved weirdly. When we looked at its chemical structure, we saw that it was the only compound in the dataset which contained a fluorine atom. What would happen if we removed the compound from the dataset The quahty ofleaming became essentially improved. It is sufficient to say that the cross-vahdation coefficient in-CTeased from 0.82 to 0.92, while the error decreased from 0.65 to 0.44. Another learning method, the Kohonen s Self-Organizing Map, also failed to classify this 31st compound correctly. Hence, we had to conclude that the compound containing a fluorine atom was an obvious outlier of the dataset. 

We keep learning more about the history of noise calculations. It seems that the topic of the noise of a spectrum in the constant-detector-noise case was addressed more than 50 years ago [1], Not only that, but it was done while taking into account the noise of the reference readings. The calculation of the optimum absorbance value was performed using several different criteria for optimum . One of these criteria, which Cole called the Probable Error Method, gives the same results that we obtained for the optimum transmittance value of 32.99%T [2], Cole s approach, however, had several limitations. The main one, from our point of view, is the fact that he directed his equations to represent the absorbance noise as soon as possible in his derivation. Thus his derivation, as well as virtually all the ones since then, bypassed consideration of the behavior of noise of transmittance spectra. This, coupled with the fact that the only place we have found that presented an expression for transmittance noise had a typographical error as we reported in our previous column [3], means that as far as we know, the correct expression for the behavior of transmittance noise has still never been previously reported in the literature. On the other hand, we do have to draw back a bit and admit that the correct expression for the optimum transmittance has been reported. 

It is also salutary to note figure 2, which reminds us that agreement and correctness are not always linked. [This figure is from the on-line dBase of particle properties http //pdg.lbl.gov.] Systematic errors always exist, and may be much larger in amplitude than expected. In general, deducing from uncertain data that a model is acceptable is not useful scientific progress. One learns from the failure of models, not from their successes. 

After a first random initialization of the values, a learning procedure modifies the weights and iVj during several optimization cycles, in order to improve the performances of the net. The correction of the weights at each step is proportional to the prediction error of the previous cycle. The optimization of many parameters and the elevated number of learning cycles considerably increase the risk of overfitting and, for this reason, a deep validation is required, with a consistent number of objects. 

Reinforcement learning the network knows whether its output is correct or not but there is no measure of the error. The weights will increase after positive behaviours or decrease (be punished) after negative behaviours. Hence positive behaviours (or behaviours reinforced positively) are learnt, whereas negatively reinforced behaviours are punished. 

---