A weak binary classifier configured to receive an input signal for classification and generate a classification output is disclosed. The weak binary classifier includes a plurality of weighting amplifier stages, each weighting amplifier stage being configured to receive the input signal for classification and a weighting input derived from a classifier model and generate a weighted input signal, the plurality of weighting amplifier stages being configured to generate a plurality of positive weighted input signals coupled to a positive summing node and a plurality of negative weighted input signals coupled to a negative summing node. The weak binary classifier also includes a comparator having a non-inverting input coupled to the positive summing node and an inverting input coupled to the negative summing node and being configured to generate a weak classification output based on the plurality of weighted input signals.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A weak binary classifier configured to receive an input signal for classification and generate a classification output, the weak binary classifier comprising: a plurality of weighting amplifier stages, each weighting amplifier stage being configured to receive the input signal for classification and a weighting input derived from a classifier model and generate a weighted input signal, the plurality of weighting amplifier stages being configured to generate a plurality of positive weighted input signals coupled to a positive summing node and a plurality of negative weighted input signals coupled to a negative summing node; a comparator having a non-inverting input coupled to the positive summing node and an inverting input coupled to the negative summing node and being configured to generate a weak classification output based on the plurality of weighted input signals. 2. The weak binary classifier of claim 1 wherein each weighting amplifier stage has an associated polarity switch configured to couple the weighted input signal of its associated weighting amplifier to the positive or negative summing node based on a polarity of the weighting input. 3. The weak binary classifier of claim 1 wherein the weighting input derived from the classifier model is a multi-bit weighting input. 4. The weak binary classifier of claim 3 wherein the weighting amplifier stage has positive and negative weighting inputs configured to receive the multi-bit input weighting input. 5. The weak binary classifier of claim 1 wherein each weighting amplifier stage includes a positive weighting amplifier and a negative weighting amplifier associated with a bit position of the weighting input. 6. A strong binary classifier including a plurality of weak binary classifiers of claim 1, the strong binary classifier comprising a digital voter having a summing input coupled to the weak classification output of each weak binary classifier, the digital voter being configured to generate a strong binary classification output. 7. The strong binary classifier of claim 6 further comprising a trainer configured to generate weighting values for each weak binary classifier. 8. The strong binary classifier of claim 7 wherein the trainer is configured to generate a first weighting input for a first weak binary classifiers and subsequent weighting inputs for subsequent weak binary classifiers, the subsequent weighting inputs are configured to correct misclassifications associated with the first weighting input. 9. The strong binary classifier of claim 7 wherein the trainer is configured to correct to non-idealities associated with the weak binary classifier. 10. The strong binary classifier of claim 7 wherein the trainer is configured to perform Error Adaptive Classifier Boosting (EACB). 11. A method of performing classification using a weak binary classifier configured to receive an input signal for classification and generate a classification output, the binary classifier comprising: providing a plurality of weighting amplifier stages, each weighting amplifier stage being configured to receive the input signal for classification and a weighting input derived from a classifier model and generate a weighted input signal, the plurality of weighting amplifier stages being configured to generate a plurality of positive weighted input signals coupled to a positive summing node and a plurality of negative weighted input signals coupled to a negative summing node; providing a comparator having a non-inverting input coupled to the positive summing node and an inverting input coupled to the negative summing node and being configured to generate a weak classification output based on the plurality of weighted input signals. 12. The method of claim 11 wherein each weighting amplifier stage has an associated polarity switch configured to couple the weighted input signal of its associated weighting amplifier to the positive or negative summing node based on a polarity of the weighting input. 13. The method of claim 11 wherein the weighting input derived from the classifier model is a multi-bit weighting input. 14. The method of claim 13 wherein the weighting amplifier stage has positive and negative weighting inputs configured to receive the multi-bit input weighting input. 15. The method of claim 11 wherein each weighting amplifier stage includes a positive weighting amplifier and a negative weighting amplifier associated with a bit position of the weighting input. 16. A method of performing strong binary classification using a plurality of weak binary classifiers of claim 11, the method comprising providing a digital voter having a summing input coupled to the weak classification output of each weak binary classifier, the digital voter being configured to generate a strong binary classification output. 17. The method of claim 16 further comprising providing a trainer configured to generate weighting values for each weak binary classifier. 18. The method of claim 17 wherein the trainer is configured to generate a first weighting input for a first weak binary classifiers and subsequent weighting inputs for subsequent weak binary classifiers, the subsequent weighting inputs are configured to correct misclassifications associated with the first weighting input. 19. The method of claim 17 wherein the trainer is configured to correct to non-idealities associated with the weak binary classifier. 20. The method of claim 17 wherein the trainer is configured to perform Error Adaptive Classifier Boosting (EACB).