Method and apparatus for input classification using non-spherical neurons

A training method for creating a new neuron in a feature space having at least one existing neuron, comprising the steps of:

• (a) generating a feature vector representative of a training input, wherein said training input corresponds to one of a plurality of possible outputs;
• (b) determining whether any existing neuron corresponding to said training input encompasses said feature vector;
• and
• (c) creating said new neuron in accordance with the determination of step (b), wherein:
• said new neuron comprises a boundary defined by two or more neuron axes;
• the length of at least one of said two or more neuron axes differs from the length of at least one other of said two or more neuron axes;
• said feature space comprises an existing feature vector;
• step (c) comprises the steps of:
• (i) creating a temporary neuron comprising a boundary defined by two or more tempQrary neuron axes;
• and
• (ii) if said temporary neuron encompasses said existing feature vector and said existing feature vector does not correspond to said training input then spatially adjusting said temporary neuron to create said new neuron;
• and
• step (c)(ii) comprises the steps of:
• (1) selecting at least one of said temporary neuron axes;
• (2) calculating the distances along each of said selected temporary neuron axes from the center of said temporary neuron to said existing feature vector;
• and
• (3) reducing said selected temporary neuron axes by amounts proportional to said distances and the lengths of said selected temporary neuron axes to create said new neuron.

A training method for adjusting a neuron, comprising the steps of:

• (a) generating a feature vector representative of a training input, wherein said training input corresponds to one of a plurality of possible outputs;
• (b) determining whether said neuron encompasses said feature vector and whether said neuron does not correspond to said training input;
• and
• (c) spatially adjusting said neuron in accordance with the determination of step (b), wherein:
• said adjusted neuron comprises a boundary defined by two or more adjusted neuron axes;
• the length of at least one of said two or more neuron axes differs from the length of at least one other of said two or more neuron axes;
• said neuron is in a feature space comprising an existing feature vector;
• said neuron comprises a boundary defined by two or more neuron axes;
• and
• step (c) further comprises the steps of:
• (i) selecting at least one of said neuron axes;
• (ii) calculating the distances along each of said selected neuron axes from the center of said neuron to said existing feature vector;
• and
• (iii) reducing said selected neuron axes by amounts proportional to said distances and the lengths of said selected neuron axes to create said adjusted neuron.

A classification apparatus for classifying an input into one of a plurality of possible outputs, comprising:

• comparing means for comparing information representative of said input to a neuron, wherein said neuron comprises a boundary defined by two or more neuron axes, wherein the length of at least one of said two or more neuron axes differs from the length of at least one other of said two or more neuron axes;
• and
• selecting means for selecting one of said possible outputs as corresponding to said input in accordance with the comparison by said comparing means;
• wherein:
• said comparing means compares information representative of said input to a plurality of neurons;
• each neuron of said plurality of neurons comprises a boundary defined by two or more neuron axes;
• the length of at least one of said two or more neuron axes differs from the length of at least one other of said two or more neuron axes;
• said information representative of said input comprises a feature vector;
• said comparing means selects each neuron that encompasses said feature vector;
• said selecting means determines a first number that is a function of the number of said selected neurons that are associated with a first possible output of said plurality of possible outputs;
• said selecting means determines a second number that is a function of the number of said selected neurons that are associated with a second possible output of said plurality of possible outputs;
• if said first number is greater than said second number, then said selecting means determines that said input does not correspond to said second possible output, else if said second number is greater than said first number, then said selecting means determines that said input does not correspond to said first possible output.

A training apparatus for creating a new neuron in a feature space having at least one existing neuron, comprising:

• generating means for generating a feature vector representative of a training input, wherein said training input corresponds to one of a plurality of possible outputs;
• and
• creating means for creating said new neuron, if no existing neuron corresponding to said training input encompasses said feature vector, wherein:
• said new neuron comprises a boundary defined by two or more neuron axes;
• the length of at least one of said two or more neuron axes differs from the length of at least one other of said two or more neuron axes;
• said feature space comprises an existing feature vector;
• said creating means creates a temporary. neuron comprising a boundary defined by two or more temporary neuron axes;
• if said temporary neuron encompasses said existing feature vector and said existing feature vector does not correspond to said training input, then said creating means spatially adjusts said temporary neuron to create said new neuron;
• said creating means selects at least one of said temporary neuron axes;
• said creating means calculates the distances along each of said selected temporary neuron axes from the center of said temporary neuron to said existing feature vector;
• and
• said creating means reduces said selected temporary neuron axes by amounts proportional to said distances and the lengths of said selected temporary neuron axes to create said new neuron.

A training apparatus for adjusting a neuron, comprising:

• generating means for generating a feature vector representative of a training input, wherein said training input corresponds to one of a plurality of possible outputs;
• and
• adjusting means for spatially adjusting said neuron, if said neuron encompasses said feature vector and said neuron does not correspond to said training input, wherein:
• said adjusted neuron comprises a boundary defined by two or more adjusted neuron axes;
• the length of at least one of said two or more neuron axes differs from the length of at least one other of said two or more neuron axes;
• said neuron is in a feature space comprising an existing feature vector;
• said neuron comprises a boundary defined by two or more neuron axes;
• said adjusting means selects at least one of said neuron axes;
• said adjusting means calculates the distances along each of said selected neuron axes from the center of said neuron to said existing feature vector;
• and
• said adjusting means reduces said selected neuron axes by amounts proportional to said distances and the lengths of said selected neuron axes to create said adjusted neuron.

A classification method for classifying an input into one of a plurality of possible outputs, comprising the steps of:

• (a) comparing information representative of said input to a neuron, wherein:
• said neuron comprises a boundary defined by two or more neuron axes;
• and
• the length of at least one of said two or more neuron axes differs from the length of at least one other of said two or more neuron axes;
• and
• (b) selecting one of said possible outputs as corresponding to said input in accordance with the comparison of step (a), wherein:
• step (a) comprises the step of comparing information representative of said input to a plurality of neurons;
• each neuron of said plurality of neurons comprises a boundary defined by two or more neuron axes;
• the length of at least one of said two or more neuron axes differs from the length of at least one other of said two or more neuron axes;
• said information representative of said input comprises a feature vector;
• step (a) further comprises the step of determining distance measures from said feature vector to each of said neurons;
• and
• step (b) comprises the steps of:
• (i) selecting a neuron of said plurality of neurons having the smallest distance measure of said distance measures;
• and
• (ii) selecting a possible output of said plurality of possible outputs that is associated with said selected neuron as corresponding to said input.

A classification apparatus for classifying an input into one of a plurality of possible outputs, comprising:

• comparing means for comparing information representative of said input to a neuron, wherein:
• said neuron comprises a boundary defined by two or more neuron axes;
• and
• the length of at least one of said two or more neuron axes differs from the length of at least one other of said two or more neuron axes;
• and
• selecting means for selecting one of said possible outputs as corresponding to said input in accordance with the comparison by said comparing means, wherein:
• said comparing means compares information representative of said input to a plurality of neurons;
• each neuron of said plurality of neurons comprises a boundary defined by two or more neuron axes;
• the length of at least one of said two or more neuron axes differs from the length of at least one other of said two or more neuron axes;
• said information representative of said input comprises a feature vector;
• said comparing means determines distance measures from said feature vector to each of said neurons;
• said selecting means selects a neuron of said plurality of neurons having the smallest distance measure of said distance measures;
• and
• said selecting means selects a possible output of said plurality of possible outputs that is associated with said selected neuron as corresponding to said input.